{"id":98136,"date":"2026-10-02T09:34:34","date_gmt":"2026-10-02T06:34:34","guid":{"rendered":"https:\/\/twodots.gr\/?p=98136"},"modified":"2026-10-02T09:34:35","modified_gmt":"2026-10-02T06:34:35","slug":"ici-time-vision-model-provlepei-chronoseires","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/ici-time-vision-model-provlepei-chronoseires\/","title":{"rendered":"ICI-Time: \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 vision model \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03c3\u03b1\u03bd \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u0391\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ce\u03c4\u03b1:<\/strong> \u03a4\u03bf ICI-Time \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 vision model \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03ad\u03c8\u03b9\u03bc\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b7 \u03ac\u03b3\u03bd\u03c9\u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c3\u03b5 \u03ba\u03b5\u03bd\u03cc \u03c0\u03c1\u03bf\u03c2 \u03c3\u03c5\u03bc\u03c0\u03bb\u03ae\u03c1\u03c9\u03c3\u03b7. \u03a4\u03bf \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03b3\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03c4\u03bf\u03c7\u03ae \u03bc\u03b5 \u03bb\u03af\u03b3\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03b1\u03bb\u03bb\u03ac \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 pilot, \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03cc validation \u03ba\u03b1\u03b9 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03bb\u03ac\u03b8\u03bf\u03c5\u03c2.<\/p>\n<\/div>\n<p>\u03a4\u03bf ICI-Time \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03c3\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af visual in-context learning \u03b3\u03b9\u03b1 forecasting \u03bc\u03b5 \u03bb\u03af\u03b3\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf pilot. \u03a4\u03b9 \u03b8\u03b1 \u03b3\u03b9\u03bd\u03cc\u03c4\u03b1\u03bd \u03b1\u03bd \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u00ab\u03b4\u03b9\u03ac\u03b2\u03b1\u03b6\u03b5\u00bb \u03c4\u03b9\u03c2 \u03c0\u03c9\u03bb\u03ae\u03c3\u03b5\u03b9\u03c2, \u03c4\u03b7 \u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03ae \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03ac\u03bb\u03c9\u03c3\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2 \u03c9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03cd\u03c2, \u03b1\u03bb\u03bb\u03ac \u03c9\u03c2 \u03bc\u03b9\u03b1 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03bf\u03c0\u03bf\u03af\u03b1 \u03bb\u03b5\u03af\u03c0\u03b5\u03b9 \u03c4\u03bf \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03bf \u03ba\u03bf\u03bc\u03bc\u03ac\u03c4\u03b9; \u0391\u03c5\u03c4\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b9\u03b4\u03ad\u03b1 \u03c4\u03bf\u03c5 ICI-Time, \u03b5\u03bd\u03cc\u03c2 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03bf\u03cd framework \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ce\u03bd \u03c3\u03b5 visual inpainting. \u0391\u03bd\u03c4\u03af \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c3\u03b5\u03b9 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ae temporal \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae, \u03b4\u03af\u03bd\u03b5\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c0\u03c1\u03bf\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf large vision model \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03bc\u03c0\u03c5\u03bb\u03ce\u03bd \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5 \u03b6\u03b7\u03c4\u03ac \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ac\u03b3\u03bd\u03c9\u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c4\u03c9\u03bd Thang Nguyen, Dung Nguyen, Romero Morais \u03ba\u03b1\u03b9 Truyen Tran \u03b1\u03c0\u03cc \u03c4\u03bf Deakin University \u03c5\u03c0\u03bf\u03b2\u03bb\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c9\u03c2 arXiv preprint \u03c4\u03bf\u03bd \u0391\u03cd\u03b3\u03bf\u03c5\u03c3\u03c4\u03bf \u03c4\u03bf\u03c5 2026. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03b5 Informer, Pyraformer \u03ba\u03b1\u03b9 LogTrans \u03c3\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03b4\u03b7\u03bc\u03b9\u03bf\u03bb\u03bf\u03b3\u03af\u03b1\u03c2, \u03ba\u03b1\u03b9\u03c1\u03bf\u03cd \u03ba\u03b1\u03b9 \u03c3\u03c5\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03b9\u03c3\u03c7\u03cd\u03bf\u03c2, \u03c7\u03c9\u03c1\u03af\u03c2 fine-tuning \u03c4\u03bf\u03c5 vision model. \u0399\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03bf \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03ad\u03c7\u03bf\u03c5\u03bd \u03c4\u03b1 few-shot \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1, \u03cc\u03c0\u03bf\u03c5 \u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c4\u03bf\u03c5 ICI-Time \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c0\u03b9\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03ba\u03b1\u03b8\u03ce\u03c2 \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03bd \u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1.<\/p>\n<p>\u0393\u03b9\u03b1 e-commerce, marketing \u03ba\u03b1\u03b9 operations teams, \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03c4\u03bf\u03b9\u03bc\u03b7 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5 demand forecasting. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 cross-modal engineering: \u03bc\u03b9\u03b1 \u03c0\u03b9\u03c3\u03c4\u03ae \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03b9\u03ba\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 foundation model \u03c3\u03b5 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03bf\u03c0\u03bf\u03af\u03b1 \u03b4\u03b5\u03bd \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5. \u0397 \u03b1\u03be\u03af\u03b1 \u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc retrieval, \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7, \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c1\u03b9\u03b8\u03bc\u03ce\u03bd \u03b1\u03c0\u03cc pixels, baselines \u03ba\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a0\u03ce\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03b1\u03c3\u03c4\u03bf\u03cd\u03bd \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1:<\/strong> \u03bf\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b7\u03bd \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 v1 \u03c4\u03bf\u03c5 paper, \u03ad\u03be\u03b9 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03b1 datasets \u03ba\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 Transformer baselines. \u0394\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 retail dataset, latency benchmark, \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc compute cost \u03ae \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf setup \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03b5\u03af promotions, stockouts, \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03ac\u03bb\u03bb\u03bf\u03c5\u03c2 \u03b1\u03b9\u03c4\u03b9\u03ce\u03b4\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03bd\u03c4\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03b6\u03ae\u03c4\u03b7\u03c3\u03b7\u03c2.<\/aside>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u0421\u044a\u0434\u044a\u0440\u0436\u0430\u043d\u0438\u0435<\/div>\n<ul>\n<li><a href=\"#optiko-keno\">\u0391\u03c0\u03cc \u03c4\u03b7 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ac \u03c3\u03c4\u03bf \u03bf\u03c0\u03c4\u03b9\u03ba\u03cc \u03ba\u03b5\u03bd\u03cc<\/a><\/li>\n<li><a href=\"#tessera-stadia\">\u03a4\u03b1 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c4\u03bf\u03c5 ICI-Time<\/a><\/li>\n<li><a href=\"#retrieval-anaparastasi\">Retrieval \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf forecast<\/a><\/li>\n<li><a href=\"#pixels-se-dedomena\">\u03a0\u03ce\u03c2 \u03c4\u03b1 pixels \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03be\u03b1\u03bd\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1<\/a><\/li>\n<li><a href=\"#peiramatiki-diataksi\">\u03a4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5<\/a><\/li>\n<li><a href=\"#full-data\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 full-data \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1<\/a><\/li>\n<li><a href=\"#few-shot\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 few-shot \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd<\/a><\/li>\n<li><a href=\"#ablations-oria\">Ablations, leakage \u03ba\u03b1\u03b9 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7\u03c2<\/a><\/li>\n<li><a href=\"#epicheirimatiki-axia\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 e-commerce \u03ba\u03b1\u03b9 predictive analytics<\/a><\/li>\n<li><a href=\"#pilot-vimata\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf pilot<\/a><\/li>\n<li><a href=\"#symperasma\">\u0397 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b4\u03b5\u03bd \u03ba\u03b1\u03c4\u03b1\u03c1\u03b3\u03b5\u03af \u03c4\u03b7 \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"optiko-keno\">\u0391\u03c0\u03cc \u03c4\u03b7 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ac \u03c3\u03c4\u03bf \u03bf\u03c0\u03c4\u03b9\u03ba\u03cc \u03ba\u03b5\u03bd\u03cc<\/h2>\n<p>\u0397 \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ce\u03bd \u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b8\u03c5\u03c1\u03bf \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2. \u03a4\u03b1 \u03c3\u03cd\u03b3\u03c7\u03c1\u03bf\u03bd\u03b1 deep learning \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c0\u03b5\u03c4\u03cd\u03c7\u03bf\u03c5\u03bd \u03c5\u03c8\u03b7\u03bb\u03ae \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1, \u03b1\u03bb\u03bb\u03ac \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ae \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae, training \u03ba\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf domain. \u0397 \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03af\u03b1 \u03bc\u03b5\u03b3\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae, \u03c3\u03c5\u03bd\u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03bd \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2 \u03ae \u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bb\u03af\u03b3\u03b1.<\/p>\n<p>\u03a4\u03bf ICI-Time \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c4\u03cd\u03c0\u03c9\u03c3\u03b7. \u039c\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03b1 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03ac\u03b8\u03c5\u03c1\u03b1 \u03c3\u03b5 area charts, \u03b2\u03ac\u03b6\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc input-output \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ce\u03c4\u03b7 \u03c3\u03b5\u03b9\u03c1\u03ac \u03b5\u03bd\u03cc\u03c2 grid \u03ba\u03b1\u03b9 \u03c4\u03bf query \u03c3\u03c4\u03b7 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7. \u0397 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae \u03c4\u03bf\u03c5 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03bf\u03cd query \u03bc\u03ad\u03bd\u03b5\u03b9 \u03ba\u03b5\u03bd\u03ae. \u03a4\u03bf vision model \u03c0\u03b1\u03c1\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c4\u03b7 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03b3\u03bd\u03c9\u03c3\u03c4\u03bf\u03cd \u03b6\u03b5\u03cd\u03b3\u03bf\u03c5\u03c2, \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03ba\u03b5\u03bd\u03cc \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b7 \u03c0\u03af\u03c3\u03c9 \u03c3\u03b5 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03cd\u03c2.<\/p>\n<p>\u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 visual in-context learning: \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf prompt \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c4\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd. \u03a4\u03bf paper \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf vision model off-the-shelf, \u03c7\u03c9\u03c1\u03af\u03c2 task-specific fine-tuning \u03ae \u03c4\u03c1\u03bf\u03c0\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2. \u0391\u03c5\u03c4\u03cc \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf ICI-Time \u03c4\u03cc\u03c3\u03bf \u03b1\u03c0\u03cc \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 temporal forecasters \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc vision-first \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 task-aligned pretraining.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid-three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Temporal Transformer<\/p>\n<p>\u039c\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c0\u03b5\u03c5\u03b8\u03b5\u03af\u03b1\u03c2 \u03b1\u03c0\u03cc \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af training \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b1 \u03cc\u03c4\u03b1\u03bd \u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0391\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03b5\u03af\u03c3\u03bf\u03b4\u03bf\u03c2<\/span><span class=\"td-badge\">Training<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">ICI-Time<\/p>\n<p>\u039c\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03c3\u03b5 \u03bf\u03c0\u03c4\u03b9\u03ba\u03cc prompt. \u03a4\u03bf vision model \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ac\u03b3\u03bd\u03c9\u03c3\u03c4\u03b7 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae \u03ba\u03b1\u03b9 \u03c4\u03bf pipeline \u03b1\u03bd\u03b1\u03ba\u03c4\u03ac \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03c3\u03b5\u03b9\u03c1\u03ac.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Visual in-context<\/span><span class=\"td-badge\">\u03a7\u03c9\u03c1\u03af\u03c2 fine-tuning<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">Nearest neighbour<\/p>\n<p>\u0391\u03bd\u03c4\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c4\u03bf\u03c5 \u03ba\u03bf\u03bd\u03c4\u03b9\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03bf\u03cd \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03bf\u03c2. \u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c9\u03c2 sanity check \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03b5\u03af \u03bc\u03b5 \u03c4\u03b7\u03bd \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03c4\u03bf LVM.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0391\u03c0\u03bb\u03cc baseline<\/span><span class=\"td-badge\">\u03a7\u03c9\u03c1\u03af\u03c2 generation<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"tessera-stadia\">\u03a4\u03b1 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03c3\u03c4\u03ac\u03b4\u03b9\u03b1 \u03c4\u03bf\u03c5 ICI-Time<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03b2\u03ac\u03c3\u03b7 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03c9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc. \u039c\u03b5 sliding windows, \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b1\u03bb\u03b1\u03b9\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03bc\u03ae\u03bc\u03b1 \u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 lookback \u03ba\u03b1\u03b9 \u03b3\u03bd\u03c9\u03c3\u03c4\u03ae \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1. \u0393\u03b9\u03b1 \u03c4\u03bf \u03bd\u03ad\u03bf query, \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03b7\u03bc\u03ad\u03bd\u03b1 inputs \u03bc\u03b5 \u03b5\u03c5\u03ba\u03bb\u03b5\u03af\u03b4\u03b5\u03b9\u03b1 \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03c4\u03bf \u03c0\u03bb\u03b7\u03c3\u03b9\u03ad\u03c3\u03c4\u03b5\u03c1\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1.<\/p>\n<p>\u03a3\u03c4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf, \u03c4\u03bf example \u03ba\u03b1\u03b9 \u03c4\u03bf query \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2. \u03a3\u03c4\u03bf \u03c4\u03c1\u03af\u03c4\u03bf, \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 grid prompt \u03ba\u03b1\u03b9 \u03c4\u03bf large vision model \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03af inpainting. \u03a3\u03c4\u03bf \u03c4\u03ad\u03c4\u03b1\u03c1\u03c4\u03bf, \u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03ba\u03b1\u03b8\u03b1\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03b7 \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b7 \u03b1\u03bd\u03b1\u03ba\u03c4\u03ac\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1. \u0397 \u03c1\u03bf\u03ae \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 case-based retrieval, \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03ba\u03c9\u03b4\u03b9\u03ba\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7, generative inference \u03ba\u03b1\u03b9 deterministic post-processing.<\/p>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03b1\u03bb\u03c5\u03c3\u03af\u03b4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03b5\u03c5\u03b8\u03cd\u03bd\u03b7\u03c2. \u0388\u03bd\u03b1 \u03ba\u03b1\u03ba\u03cc forecast \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03c5\u03c0\u03bf\u03c7\u03c1\u03b5\u03c9\u03c4\u03b9\u03ba\u03ac \u03b1\u03c0\u03cc \u03c4\u03bf vision model. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bf\u03c6\u03b5\u03af\u03bb\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b1\u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03b1\u03c3\u03c5\u03bd\u03b5\u03c0\u03ae \u03ac\u03be\u03bf\u03bd\u03b1, \u03b1\u03bd\u03b5\u03c0\u03b1\u03c1\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2, \u03b8\u03cc\u03c1\u03c5\u03b2\u03bf \u03c3\u03c4\u03bf inpainting \u03ae \u03bb\u03ac\u03b8\u03bf\u03c2 curve extraction. \u038c\u03c0\u03c9\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd <a href=\"https:\/\/twodots.gr\/explainable-ai-xronoseires-frameworks\/\">Explainable AI \u03b3\u03b9\u03b1 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2<\/a>, \u03b7 \u03b5\u03be\u03ae\u03b3\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf pipeline \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc predictor.<\/p>\n<h2 id=\"retrieval-anaparastasi\">Retrieval \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b8\u03bf\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf forecast<\/h2>\n<p>\u03a4\u03bf in-context example \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c3\u03b1\u03bd \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b1. \u0391\u03bd \u03b7 \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b7 \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b7 \u03bc\u03bf\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03bc\u03bf\u03c4\u03af\u03b2\u03bf, \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03c0\u03ce\u03c2 \u03b5\u03be\u03b5\u03bb\u03af\u03c7\u03b8\u03b7\u03ba\u03b5 \u03b5\u03ba\u03b5\u03af\u03bd\u03bf \u03c4\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03cc\u03c3\u03b5\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c3\u03c4\u03bf query. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd \u03c4\u03bf LVM \u03c9\u03c2 \u03bc\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc interpolator \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03c9\u03bd \u03c3\u03c4\u03bf\u03bd \u03c7\u03ce\u03c1\u03bf \u03c4\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2.<\/p>\n<p>\u03a3\u03b5 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03cc \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1, \u03b7 \u03b1\u03c0\u03bb\u03ae \u03bf\u03bc\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 lookback \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03cc \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf. \u039c\u03b9\u03b1 \u03ba\u03bf\u03c1\u03cd\u03c6\u03c9\u03c3\u03b7 \u03c0\u03c9\u03bb\u03ae\u03c3\u03b5\u03c9\u03bd \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc Black Friday \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03bf\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5 \u03ba\u03bf\u03c1\u03cd\u03c6\u03c9\u03c3\u03b7 \u03b1\u03c0\u03cc stock clearance, \u03b5\u03bd\u03ce \u03b7 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ba\u03af\u03bd\u03b7\u03c3\u03b7 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bd\u03ad\u03b1 \u03b1\u03b3\u03bf\u03c1\u03ac: \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/geographic-domain-shift-ai-modelo-se-nees-agores\/\">geographic domain shift<\/a> \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03c4\u03b7 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03bc\u03bf\u03c4\u03af\u03b2\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ae \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac.<\/p>\n<p>\u0397 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03ad\u03c8\u03b9\u03bc\u03b7. \u03a4\u03bf ICI-Time \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03b9 min-max normalization \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c4\u03b1 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5 query, \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03c3\u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1. \u039f\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ad\u03c7\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 224 \u00d7 224 pixels. \u0397 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 input \u03ba\u03b1\u03b9 output \u03c1\u03c5\u03b8\u03bc\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03cc\u03c1\u03b9\u03b1 \u03c3\u03c4\u03bf\u03bd \u03bf\u03c1\u03b9\u03b6\u03cc\u03bd\u03c4\u03b9\u03bf \u03ac\u03be\u03bf\u03bd\u03b1, \u03b5\u03bd\u03ce \u03b7 \u03ba\u03ac\u03b8\u03b5\u03c4\u03b7 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1 \u03c4\u03bf\u03c5 example \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf query.<\/p>\n<p>\u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b1\u03c5\u03c4\u03ae \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03b9\u03ba\u03ae \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1. \u0391\u03bd \u03bc\u03b9\u03ba\u03c1\u03ae \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c4\u03cc\u03c0\u03b9\u03c3\u03b7 pixel \u03ae \u03b1\u03bd example \u03ba\u03b1\u03b9 query \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03b1\u03c3\u03cd\u03bd\u03b4\u03b5\u03c4\u03b5\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2, \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03b1\u03c3\u03c4\u03b1\u03b8\u03ae \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 versioning \u03ba\u03b1\u03b9 tests \u03cc\u03c0\u03c9\u03c2 \u03ba\u03ac\u03b8\u03b5 \u03ac\u03bb\u03bb\u03bf production feature.<\/p>\n<h2 id=\"pixels-se-dedomena\">\u03a0\u03ce\u03c2 \u03c4\u03b1 pixels \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03be\u03b1\u03bd\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1<\/h2>\n<p>\u039c\u03b5\u03c4\u03ac \u03c4\u03bf inpainting, \u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03ad\u03c7\u03b5\u03b9 \u03b8\u03cc\u03c1\u03c5\u03b2\u03bf \u03ae \u03b1\u03c3\u03b1\u03c6\u03ae \u03cc\u03c1\u03b9\u03b1. \u03a4\u03bf framework \u03ba\u03ac\u03bd\u03b5\u03b9 binarization, \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ae \u03ba\u03b1\u03b9 morphological opening \u03bc\u03b5 \u03b4\u03bf\u03bc\u03b9\u03ba\u03cc \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03bf 3 \u00d7 3 pixels. \u0388\u03c0\u03b5\u03b9\u03c4\u03b1 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03b9 \u03c4\u03bf \u03b1\u03bd\u03ce\u03c4\u03b5\u03c1\u03bf foreground pixel \u03ba\u03ac\u03b8\u03b5 \u03c3\u03c4\u03ae\u03bb\u03b7\u03c2, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c3\u03c5\u03bd\u03b5\u03c7\u03ad\u03c2 \u03c0\u03b5\u03c1\u03af\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1 \u03bc\u03b5 interpolation \u03ba\u03b1\u03b9 \u03c4\u03bf \u03b4\u03b5\u03b9\u03b3\u03bc\u03b1\u03c4\u03bf\u03bb\u03b7\u03c0\u03c4\u03b5\u03af \u03c3\u03c4\u03bf\u03bd \u03b6\u03b7\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ce\u03bd \u03b2\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd.<\/p>\n<p>\u03a5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c4\u03bf \u00abdisconnection problem\u00bb: \u03c4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03c0\u03c1\u03bf\u03b2\u03bb\u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03ad\u03c7\u03b5\u03b9 \u03b1\u03c0\u03cc\u03c4\u03bf\u03bc\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03bf \u03b3\u03bd\u03c9\u03c3\u03c4\u03cc \u03c3\u03b7\u03bc\u03b5\u03af\u03bf. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03bf\u03c5\u03bd Gaussian smoothing decay \u03c0\u03bf\u03c5 \u03b5\u03bd\u03ce\u03bd\u03b5\u03b9 \u03bf\u03bc\u03b1\u03bb\u03ac \u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 10 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1. \u03a3\u03c4\u03b9\u03c2 \u03ad\u03be\u03b9 \u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03c4\u03bf smoothing \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf \u03bc\u03ad\u03c3\u03bf MSE \u03ba\u03b1\u03c4\u03ac 0,7% \u03ad\u03c9\u03c2 2,2%, \u03bc\u03b5 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03c3\u03c4\u03bf ILI.<\/p>\n<p>\u0386\u03c1\u03b1 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ba\u03b1\u03b9 post-processing. \u039c\u03b9\u03b1 \u03b4\u03af\u03ba\u03b1\u03b9\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf pipeline \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03b5 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b1 baselines. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c1\u03b8\u03cc \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03af\u03b4\u03b5\u03c4\u03b1\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf foundation model, \u03bf\u03cd\u03c4\u03b5 \u03bd\u03b1 \u03b1\u03b3\u03bd\u03bf\u03b5\u03af\u03c4\u03b1\u03b9 \u03b7 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf smoothing \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03b1\u03c0\u03cc\u03c4\u03bf\u03bc\u03b7 \u03b1\u03bb\u03bb\u03ac \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae.<\/p>\n<h2 id=\"peiramatiki-diataksi\">\u03a4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5<\/h2>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03ac\u03bb\u03c5\u03c8\u03b5 \u03c4\u03c1\u03af\u03b1 domains. \u03a4\u03bf ILI \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03b5\u03b2\u03b4\u03bf\u03bc\u03b1\u03b4\u03b9\u03b1\u03af\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c3\u03b8\u03b5\u03bd\u03ce\u03bd \u03bc\u03b5 \u03c3\u03c5\u03bc\u03c0\u03c4\u03ce\u03bc\u03b1\u03c4\u03b1 \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03bc\u03b5 \u03b3\u03c1\u03af\u03c0\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf CDC \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c0\u03b5\u03c1\u03af\u03bf\u03b4\u03bf 2002\u20132021. \u03a4\u03bf Weather \u03ad\u03c7\u03b5\u03b9 21 \u03bc\u03b5\u03c4\u03b5\u03c9\u03c1\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03bf\u03cd\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b5\u03c2 \u03b1\u03bd\u03ac 10 \u03bb\u03b5\u03c0\u03c4\u03ac \u03b3\u03b9\u03b1 \u03c4\u03bf 2020. \u03a4\u03bf ETT \u03b1\u03c6\u03bf\u03c1\u03ac \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b5\u03c2 \u03b4\u03cd\u03bf \u03b7\u03bb\u03b5\u03ba\u03c4\u03c1\u03b9\u03ba\u03ce\u03bd \u03bc\u03b5\u03c4\u03b1\u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03b9\u03c3\u03c4\u03ce\u03bd \u03c3\u03b5 \u03c9\u03c1\u03b9\u03b1\u03af\u03b1 \u03ba\u03b1\u03b9 15\u03bb\u03b5\u03c0\u03c4\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7, \u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 ETTh1, ETTh2, ETTm1 \u03ba\u03b1\u03b9 ETTm2.<\/p>\n<p>\u039f\u03b9 \u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1 966, 52.696, 17.420 \u03ba\u03b1\u03b9 69.680 timesteps \u03b3\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c4\u03bf\u03c5 \u03c0\u03af\u03bd\u03b1\u03ba\u03b1 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2. \u03a4\u03bf lookback \u03b5\u03af\u03bd\u03b1\u03b9 96 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03bf ICI-Time \u03ba\u03b1\u03b9 \u03c4\u03b1 Transformer baselines. \u039f\u03b9 \u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b5\u03c2 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 24, 36, 48 \u03ba\u03b1\u03b9 60 \u03b3\u03b9\u03b1 ILI \u03ba\u03b1\u03b9 96, 192, 336 \u03ba\u03b1\u03b9 720 \u03b3\u03b9\u03b1 Weather \u03ba\u03b1\u03b9 ETT.<\/p>\n<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03ad\u03b3\u03b9\u03bd\u03b5 \u03bc\u03b5 Informer, Pyraformer \u03ba\u03b1\u03b9 LogTrans, \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 MSE \u03ba\u03b1\u03b9 MAE. \u039f\u03b9 baseline \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c0\u03c1\u03bf\u03ae\u03bb\u03b8\u03b1\u03bd \u03b1\u03c0\u03cc \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03cc\u03c4\u03b1\u03bd \u03c5\u03c0\u03ae\u03c1\u03c7\u03b1\u03bd, \u03b5\u03bd\u03ce \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03c3\u03c5\u03bc\u03c0\u03bb\u03ae\u03c1\u03c9\u03c3\u03b1\u03bd \u03c4\u03b1 \u03ba\u03b5\u03bd\u03ac \u03bc\u03b5 \u03b4\u03b9\u03ba\u03ac \u03c4\u03bf\u03c5\u03c2 experiments \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c0\u03b5\u03af\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf setup \u03b5\u03af\u03bd\u03b1\u03b9 channel-independent: \u03ba\u03ac\u03b8\u03b5 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ae \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03b5\u03bb\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03bf\u03b9 \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c7\u03ad\u03c3\u03b5\u03b9\u03c2.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7. \u0397 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03b6\u03ae\u03c4\u03b7\u03c3\u03b7\u03c2 \u03c3\u03c5\u03c7\u03bd\u03ac \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03b9\u03bc\u03ae, promotion, stock availability, \u03b7\u03bc\u03ad\u03c1\u03b1, \u03ba\u03b1\u03bd\u03ac\u03bb\u03b9 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9\u03c2. \u0397 <a href=\"https:\/\/twodots.gr\/causal-ai-stin-praxi-giati-i-syschetisi-den-arkei-gia-epicheirimatikes-apofaseis\/\">Causal AI \u03c3\u03c4\u03b9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2<\/a> \u03c5\u03c0\u03b5\u03bd\u03b8\u03c5\u03bc\u03af\u03b6\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03ad\u03bd\u03b1 \u03ba\u03b1\u03bb\u03cc \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03b4\u03b5\u03bd \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03b5\u03af \u03bc\u03b5 \u03ba\u03b1\u03c4\u03b1\u03bd\u03cc\u03b7\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03b9\u03c4\u03af\u03b1\u03c2 \u03c0\u03bf\u03c5 \u03c4\u03bf \u03b4\u03b7\u03bc\u03b9\u03bf\u03cd\u03c1\u03b3\u03b7\u03c3\u03b5.<\/p>\n<h2 id=\"full-data\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 full-data \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1<\/h2>\n<p>\u03a3\u03c4\u03bf full-data setting, \u03c4\u03bf ICI-Time \u03b5\u03af\u03c7\u03b5 \u03c4\u03b7\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 MAE \u03c3\u03b5 23 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 24 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c3\u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03b1. \u03a3\u03c4\u03bf MSE \u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03ae\u03c4\u03b1\u03bd \u03c0\u03b9\u03bf \u03bc\u03b9\u03ba\u03c4\u03ae. \u03a3\u03c4\u03bf ETTh1, \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, Pyraformer \u03ae Informer \u03b5\u03af\u03c7\u03b1\u03bd \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf MSE \u03c3\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03bf\u03cd\u03c2 \u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b5\u03c2, \u03b5\u03bd\u03ce \u03c3\u03c4\u03b1 ETTh2, ETTm2 \u03ba\u03b1\u03b9 Weather \u03c4\u03bf ICI-Time \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03af\u03b1\u03c3\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1.<\/p>\n<p>\u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 MAE \u03ba\u03b1\u03b9 MSE \u03ad\u03c7\u03b5\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1. \u03a4\u03bf MSE \u03c4\u03b9\u03bc\u03c9\u03c1\u03b5\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 \u03c3\u03c6\u03ac\u03bb\u03bc\u03b1\u03c4\u03b1, \u03ac\u03c1\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03b1\u03ba\u03c1\u03b1\u03af\u03bf under-forecast \u03c0\u03c1\u03bf\u03ba\u03b1\u03bb\u03b5\u03af stockout \u03ae \u03cc\u03c4\u03b1\u03bd \u03bc\u03b9\u03b1 \u03bb\u03ac\u03b8\u03bf\u03c2 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03b9\u03c3\u03c7\u03cd\u03bf\u03c2 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2. \u03a4\u03bf MAE \u03b4\u03af\u03bd\u03b5\u03b9 \u03c0\u03b9\u03bf \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03ae \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03c4\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b7\u03b8\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03c5 \u03c3\u03c6\u03ac\u03bb\u03bc\u03b1\u03c4\u03bf\u03c2. \u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae metric \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c0\u03b9\u03bf \u03b5\u03c5\u03bd\u03bf\u03ca\u03ba\u03ac \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c3\u03b7\u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03bf \u03bc\u03ad\u03c3\u03bf\u03c2 \u03cc\u03c1\u03bf\u03c2 \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd runs \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac near-optimal examples \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf\u03bd \u03b8\u03cc\u03c1\u03c5\u03b2\u03bf. \u0391\u03c5\u03c4\u03cc \u03cc\u03bc\u03c9\u03c2 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03bf inference work \u03ba\u03b1\u03b9 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03bd\u03ad\u03b1 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7: \u03c0\u03cc\u03c3\u03b1 runs \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9, \u03c0\u03ce\u03c2 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03c4\u03bf\u03c5 forecast. \u03a4\u03bf paper \u03b4\u03b5\u03bd \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 latency \u03ae \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc compute cost \u03b1\u03bd\u03ac \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7.<\/p>\n<p>\u0388\u03bd\u03b1\u03c2 nearest-neighbour sanity check, \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03ad\u03b3\u03c1\u03b1\u03c6\u03b5 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03ce\u03c4\u03bf\u03c5 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03bf\u03c2, \u03b1\u03c0\u03ad\u03b4\u03c9\u03c3\u03b5 \u03c7\u03b5\u03b9\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf ICI-Time \u03ba\u03b1\u03b9 \u03c4\u03b1 Transformer baselines. \u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf vision model \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c4\u03bf example. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03cc\u03c4\u03b9 \u03ad\u03c7\u03b5\u03b9 \u03bc\u03ac\u03b8\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03c3\u03b5\u03b9\u03c1\u03ac.<\/p>\n<p>\u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03c0\u03b5\u03b9\u03b8\u03b1\u03c1\u03c7\u03af\u03b1\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 forecasting experiment. \u0397 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 <a href=\"https:\/\/twodots.gr\/qhadamw-provlepsi-atmosfairikis-rypansis-manila\/\">QHAdamW \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ce\u03bd<\/a> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af dataset, split, horizon, optimizer \u03ba\u03b1\u03b9 baseline \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af \u03c0\u03c1\u03b9\u03bd \u03ad\u03bd\u03b1 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf error \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03b5\u03af \u03c3\u03b5 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1.<\/p>\n<h2 id=\"few-shot\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 few-shot \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd<\/h2>\n<p>\u03a4\u03b1 few-shot experiments \u03c0\u03b5\u03c1\u03b9\u03cc\u03c1\u03b9\u03c3\u03b1\u03bd \u03cc\u03bb\u03b1 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c3\u03c4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf 10%, 5% \u03ae 1% \u03c4\u03c9\u03bd training data. \u0393\u03b9\u03b1 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c6\u03b5\u03c5\u03c7\u03b8\u03b5\u03af leakage \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03b9\u03b3\u03bc\u03ac\u03c4\u03c9\u03bd, \u03c4\u03b1 \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1 test input \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b8\u03b1 \u03ae\u03c4\u03b1\u03bd \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03b1\u03c3\u03c4\u03ac\u03b8\u03b7\u03ba\u03b1\u03bd \u03bc\u03b5 \u03bc\u03b7\u03b4\u03b5\u03bd\u03b9\u03ba\u03ac \u03ba\u03b1\u03c4\u03ac \u03c4\u03bf\u03bd \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc \u03b1\u03c0\u03bf\u03c3\u03c4\u03ac\u03c3\u03b5\u03c9\u03bd. \u03a3\u03c4\u03bf 5% \u03ba\u03b1\u03b9 1% \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03bc\u03cc\u03bd\u03bf horizons \u03bc\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b3\u03b9\u03b1 training \u03c4\u03c9\u03bd baselines.<\/p>\n<p>\u03a3\u03c4\u03bf Weather \u03bc\u03b5 horizon 96, \u03cc\u03c4\u03b1\u03bd \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03c0\u03cc 10% \u03c3\u03b5 1%, \u03c4\u03bf MAE \u03c4\u03bf\u03c5 Informer \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 0,389 \u03c3\u03b5 0,514 \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5 Pyraformer \u03b1\u03c0\u03cc 0,360 \u03c3\u03b5 0,487. \u03a4\u03bf ICI-Time \u03bc\u03b5\u03c4\u03b1\u03ba\u03b9\u03bd\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 0,234 \u03c3\u03b5 0,242. \u039f\u03b9 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c5\u03be\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 32,1%, 35,3% \u03ba\u03b1\u03b9 3,4% \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1.<\/p>\n<p>\u03a3\u03b5 \u03ac\u03bb\u03bb\u03bf \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c3\u03c4\u03bf ETTh2 \u03bc\u03b5 horizon 96 \u03ba\u03b1\u03b9 10% \u03c4\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03c4\u03bf MSE \u03ae\u03c4\u03b1\u03bd 0,317 \u03b3\u03b9\u03b1 ICI-Time, 4,047 \u03b3\u03b9\u03b1 Informer \u03ba\u03b1\u03b9 4,065 \u03b3\u03b9\u03b1 Pyraformer. \u03a3\u03c4\u03bf ETTm2 \u03bc\u03b5 horizon 96 \u03ba\u03b1\u03b9 1%, \u03bf\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd 0,208, 1,984 \u03ba\u03b1\u03b9 2,444. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c3\u03b5\u03b9\u03c1\u03ad\u03c2, splits \u03ba\u03b1\u03b9 baselines\u00b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03c4\u03bf\u03c5 visual forecasting.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-kicker\">\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c0\u03cc \u03c4\u03b1 few-shot experiments<\/p>\n<p class=\"td-chart-title\">\u0397 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 ICI-Time \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03ba\u03cd\u03c1\u03b9\u03bf \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc \u03c3\u03ae\u03bc\u03b1<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7 \u03c4\u03bf\u03c5 paper \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bd \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c3\u03b5 retail \u03ae \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03ad\u03bf validation.<\/p>\n<\/div>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,234 \u2192 0,242<\/span><span class=\"td-metric-label\">MAE \u03c4\u03bf\u03c5 ICI-Time \u03c3\u03c4\u03bf Weather, horizon 96, \u03b1\u03c0\u03cc 10% \u03c3\u03b5 1% training data<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">3,4%<\/span><span class=\"td-metric-label\">\u03a3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ae \u03b1\u03cd\u03be\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 MAE, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 32,1% \u03c4\u03bf\u03c5 Informer \u03ba\u03b1\u03b9 35,3% \u03c4\u03bf\u03c5 Pyraformer<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,317<\/span><span class=\"td-metric-label\">MSE \u03c4\u03bf\u03c5 ICI-Time \u03c3\u03c4\u03bf ETTh2, horizon 96, \u03bc\u03b5 10% data\u00b7 \u03c4\u03b1 \u03b4\u03cd\u03bf baselines \u03ae\u03c4\u03b1\u03bd \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc 4<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,208<\/span><span class=\"td-metric-label\">MSE \u03c3\u03c4\u03bf ETTm2, horizon 96, \u03bc\u03b5 1% data\u00b7 Informer 1,984 \u03ba\u03b1\u03b9 Pyraformer 2,444<\/span><\/div>\n<\/div>\n<p class=\"td-chart-source\">\u03a0\u03b7\u03b3\u03ae \u03c4\u03b9\u03bc\u03ce\u03bd: Nguyen et al., \u00abIn-Context Inpainting for Time Series Forecasting\u00bb, arXiv:2608.23855v1.<\/p>\n<\/div>\n<h2 id=\"ablations-oria\">Ablations, leakage \u03ba\u03b1\u03b9 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7\u03c2<\/h2>\n<p>\u0397 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03b7\u03c2 \u03ba\u03ac\u03b8\u03b5\u03c4\u03b7\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1\u03c2 \u03b1\u03c0\u03cc \u03c4\u03bf example \u03c3\u03c4\u03bf query \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf \u03bc\u03ad\u03c3\u03bf MSE \u03ba\u03b1\u03c4\u03ac 9,2% \u03ad\u03c9\u03c2 18,5% \u03c3\u03c4\u03b1 ETT \u03ba\u03b1\u03b9 Weather \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 fixed factor 1,5. \u03a4\u03bf ILI \u03ae\u03c4\u03b1\u03bd \u03b5\u03be\u03b1\u03af\u03c1\u03b5\u03c3\u03b7, \u03cc\u03c0\u03bf\u03c5 \u03b7 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1 \u03b1\u03c0\u03ad\u03b4\u03c9\u03c3\u03b5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1. \u0397 \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae\u03c2 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 input \u03ba\u03b1\u03b9 output \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03b7\u03bd \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b7 \u03c3\u03c5\u03bc\u03c0\u03af\u03b5\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 \u03ad\u03be\u03b9 datasets, \u03bc\u03b5 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03bc\u03ad\u03c3\u03bf\u03c5 MSE 1,8% \u03ad\u03c9\u03c2 5,1% \u03c3\u03b5 ETT \u03ba\u03b1\u03b9 Weather \u03ba\u03b1\u03b9 10,7% \u03c3\u03c4\u03bf ILI.<\/p>\n<p>\u039f\u03b9 ablations \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03b7 \u00ab\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u00bb \u03c4\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2 \u03c3\u03c5\u03bc\u03b2\u03ac\u03bb\u03bb\u03b5\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1. \u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae modality \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b1\u03b3\u03b9\u03ba\u03ae \u03c3\u03c5\u03bd\u03c4\u03cc\u03bc\u03b5\u03c5\u03c3\u03b7: \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 engineering \u03c4\u03b7\u03c2 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7\u03c2, \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 transforms \u03ba\u03b1\u03b9 tests \u03c0\u03bf\u03c5 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03ce\u03bd\u03bf\u03c5\u03bd \u03c0\u03bf\u03b9\u03bf \u03ba\u03bf\u03bc\u03bc\u03ac\u03c4\u03b9 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf forecast.<\/p>\n<p>\u0397 \u03c0\u03c1\u03cc\u03bb\u03b7\u03c8\u03b7 leakage \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03c3\u03b5 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc pilot. \u03a4\u03b1 rolling splits \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c3\u03c6\u03b1\u03bb\u03af\u03b6\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03c4\u03bf retrieval \u03b4\u03b5\u03bd \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03cc\u03c4\u03b9 normalization \u03ba\u03b1\u03b9 feature engineering \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b1\u03c0\u03cc \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03ba\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03b1\u03bd\u03ad\u03bd\u03b1 promotion \u03ae \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd \u03b4\u03b5\u03bd \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1 \u03c3\u03b5 train \u03ba\u03b1\u03b9 test \u03bc\u03b5 \u03c4\u03c1\u03cc\u03c0\u03bf \u03c0\u03bf\u03c5 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ac \u03b5\u03cd\u03ba\u03bf\u03bb\u03b7.<\/p>\n<p>\u0397 \u03c3\u03b7\u03bc\u03b5\u03c1\u03b9\u03bd\u03ae \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03ad\u03c7\u03b5\u03b9 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 \u03cc\u03c1\u03b9\u03b1: preprint v1, \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03b1 datasets, \u03c4\u03c1\u03b5\u03b9\u03c2 Transformer families, fixed \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 224 \u00d7 224, channel-independent forecasting \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c5\u03c3\u03af\u03b1 retail \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, latency \u03ba\u03b1\u03b9 compute cost. \u0397 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 curve extraction \u03ba\u03b1\u03b9 smoothing \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03ae \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03bd\u03b5\u03b9 \u03b1\u03c0\u03cc\u03c4\u03bf\u03bc\u03b5\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5 ILI \u03c3\u03c4\u03bf height ablation \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03af\u03b1 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd.<\/p>\n<h2 id=\"epicheirimatiki-axia\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 e-commerce \u03ba\u03b1\u03b9 predictive analytics<\/h2>\n<p>\u039f\u03b9 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ad\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd \u03b6\u03ae\u03c4\u03b7\u03c3\u03b7, traffic, \u03c0\u03b1\u03c1\u03b1\u03b3\u03b3\u03b5\u03bb\u03af\u03b5\u03c2, \u03c7\u03c1\u03ae\u03c3\u03b7 \u03c5\u03c0\u03b7\u03c1\u03b5\u03c3\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ac \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7. \u03a4\u03bf \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03b8\u03b5\u03af \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 SKU, \u03bc\u03b9\u03b1 \u03b1\u03b3\u03bf\u03c1\u03ac \u03ae \u03ad\u03bd\u03b1 \u03ba\u03b1\u03bd\u03ac\u03bb\u03b9 \u03ad\u03c7\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc. \u03a4\u03bf paper \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 \u03c4\u03bf visual in-context approach \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03b5 \u03c4\u03ad\u03c4\u03bf\u03b9\u03b5\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2.<\/p>\n<p>\u0394\u03b5\u03bd \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03b5 promotions, stockouts, holidays, price changes \u03ae multivariate causal signals. \u039c\u03b9\u03b1 e-commerce \u03bf\u03bc\u03ac\u03b4\u03b1 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c8\u03b5\u03b9 \u03c4\u03b9\u03c2 benchmark \u03b5\u03c0\u03b9\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 revenue \u03ae inventory accuracy. \u03a4\u03b1 <a href=\"https:\/\/twodots.gr\/market-models-genetiki-ai-emporikes-apofaseis\/\">market models \u03b3\u03b9\u03b1 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2<\/a> \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03be\u03af\u03b1 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 \u03c5\u03c0\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2, \u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03bf\u03c1\u03b1\u03c4\u03ac.<\/p>\n<p>\u0397 \u03c0\u03b9\u03b8\u03b1\u03bd\u03ae \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03c0\u03b5\u03ba\u03c4\u03b5\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf interface \u03c4\u03b7\u03c2 \u03b1\u03bd\u03b1\u03bb\u03c5\u03c4\u03b9\u03ba\u03ae\u03c2. \u0391\u03bd \u03b7 \u03af\u03b4\u03b9\u03b1 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 model inference \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf, \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b9\u03b8\u03b5\u03c9\u03c1\u03b5\u03af example, query \u03ba\u03b1\u03b9 prediction \u03c3\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03cd\u03bb\u03bf\u03b3\u03b7 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03bf\u03c0\u03c4\u03b9\u03ba\u03ae, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b5 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7. \u03a4\u03b1 <a href=\"https:\/\/twodots.gr\/ergaleia-statistikis-analysis-epicheiriseis-2026\/\">\u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2<\/a> \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b1 \u03b3\u03b9\u03b1 residuals, rolling performance \u03ba\u03b1\u03b9 uncertainty.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">\u039a\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2<\/p>\n<p class=\"td-decision-title\">\u0394\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf ICI-Time \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1, \u03cc\u03c7\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b9\u03b4\u03ad\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03ae<\/p>\n<p>\u03a0\u03c1\u03bf\u03c7\u03c9\u03c1\u03ae\u03c3\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03ad\u03bd\u03b1 time-based backtest \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 pipeline \u03bc\u03b5 seasonal naive, nearest neighbour, \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03bd production forecast \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf\u03bd \u03ad\u03bd\u03b1 \u03c3\u03cd\u03b3\u03c7\u03c1\u03bf\u03bd\u03bf baseline. \u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 business cost, \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1, latency \u03ba\u03b1\u03b9 failure rate \u03c4\u03b7\u03c2 \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b7\u03c2.<\/p>\n<\/div>\n<h2 id=\"pilot-vimata\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf pilot<\/h2>\n<p>\u0388\u03bd\u03b1 pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03b1 \u03c3\u03b5\u03b9\u03c1\u03ac \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9, \u03c0\u03cc\u03c3\u03bf \u03bc\u03b1\u03ba\u03c1\u03b9\u03ac, \u03c0\u03cc\u03c3\u03bf \u03c3\u03c5\u03c7\u03bd\u03ac \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 under-forecast \u03ba\u03b1\u03b9 over-forecast. \u03a4\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf <a href=\"https:\/\/twodots.gr\/rail-ai-4-erotimata-prin-tin-paragogi\/\">RAIL \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/a> \u03b2\u03bf\u03b7\u03b8\u03ac \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03b5\u03b8\u03bf\u03cd\u03bd reliability, accountability, inputs \u03ba\u03b1\u03b9 limits \u03bc\u03b5 \u03c3\u03b1\u03c6\u03ad\u03c2 go\/no-go.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0391\u03c0\u03cc \u03c4\u03bf \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf visual forecasting<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b7 \u03c3\u03b5\u03b9\u03c1\u03ac, \u03c4\u03bf\u03bd \u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7<\/strong>\n<p>\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf SKU, traffic metric \u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03c0\u03bf\u03b9\u03b1 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1 \u03b8\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf forecast \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c3\u03bf \u03ba\u03bf\u03c3\u03c4\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03bb\u03ac\u03b8\u03bf\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 2<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03c7\u03c1\u03bf\u03bd\u03b9\u03ba\u03cc dataset snapshot<\/strong>\n<p>\u0394\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03c4\u03b5 timestamps, missing values, calendar events, promotions \u03ba\u03b1\u03b9 stockouts, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03bf\u03c1\u03b8\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03b8\u03b1 \u03ad\u03ba\u03b1\u03bd\u03b1\u03bd \u03c4\u03bf backtest \u03b5\u03c5\u03ba\u03bf\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 3<\/span><strong>\u03a7\u03c4\u03af\u03c3\u03c4\u03b5 rolling splits \u03c7\u03c9\u03c1\u03af\u03c2 leakage<\/strong>\n<p>\u0395\u03c0\u03b9\u03c4\u03c1\u03ad\u03c8\u03c4\u03b5 \u03c3\u03c4\u03bf retrieval \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03bc\u03cc\u03bd\u03bf \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 query \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03c3\u03c4\u03b5 normalization \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf \u03c0\u03b1\u03c1\u03b5\u03bb\u03b8\u03cc\u03bd.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 4<\/span><strong>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03b1\u03c0\u03bb\u03ac \u03ba\u03b1\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ac baselines<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 seasonal naive, nearest neighbour, \u03c4\u03bf \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03bd production forecast \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf\u03bd \u03ad\u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03b5 \u03c4\u03b1 \u03af\u03b4\u03b9\u03b1 windows, horizons \u03ba\u03b1\u03b9 metrics.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 5<\/span><strong>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03cc\u03bb\u03bf \u03c4\u03bf ICI-Time pipeline<\/strong>\n<p>\u0391\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03c3\u03c4\u03b5 query window, retrieved example, normalization parameters, visual prompt, inpainted output, curve extraction \u03ba\u03b1\u03b9 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03c3\u03b5\u03b9\u03c1\u03ac \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 run.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 6<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/strong>\n<p>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 MAE, MSE, under-forecast cost, stockout risk, latency, \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc runs, failures \u03b1\u03bd\u03ac\u03ba\u03c4\u03b7\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03c5\u03b1\u03b9\u03c3\u03b8\u03b7\u03c3\u03af\u03b1 \u03c3\u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae example.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0421\u0442\u044a\u043f\u043a\u0430 7<\/span><strong>\u039a\u03ac\u03bd\u03c4\u03b5 shadow test \u03bc\u03b5 fallback<\/strong>\n<p>\u03a4\u03c1\u03ad\u03be\u03c4\u03b5 \u03c4\u03bf \u03bd\u03ad\u03bf forecast \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b5\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2, \u03bf\u03c1\u03af\u03c3\u03c4\u03b5 \u03cc\u03c1\u03b9\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03c3\u03cd\u03bd\u03b4\u03b5\u03c4\u03b5\u03c2 \u03ae \u03b1\u03ba\u03c1\u03b1\u03af\u03b5\u03c2 \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03ad\u03c8\u03c4\u03b5 \u03c3\u03b5 \u03b5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf baseline \u03cc\u03c4\u03b1\u03bd \u03bb\u03b5\u03af\u03c0\u03b5\u03b9 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03bf \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03bf.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u0397 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c6\u03b5\u03cd\u03b3\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc \u00ab\u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf score\u00bb. \u038c\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b7 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 <a href=\"https:\/\/twodots.gr\/ai-roi-axia-ergasias\/\">AI ROI \u03b1\u03bd\u03ac \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/a>, \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7\u03c2 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7\u03c2 \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 inference, \u03b5\u03c0\u03bf\u03c0\u03c4\u03b5\u03af\u03b1\u03c2, \u03bb\u03b1\u03b8\u03ce\u03bd \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03c4\u03ae\u03c1\u03b7\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"symperasma\">\u0397 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b4\u03b5\u03bd \u03ba\u03b1\u03c4\u03b1\u03c1\u03b3\u03b5\u03af \u03c4\u03b7 \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7<\/h2>\n<p>\u03a4\u03bf ICI-Time \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1\u03c2 \u03b1\u03ba\u03cc\u03bc\u03b7 forecaster. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c0\u03ce\u03c2 \u03ad\u03bd\u03b1 interface \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc foundation model: \u03b7 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ac \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c7\u03ae\u03bc\u03b1, \u03b7 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c5\u03bc\u03c0\u03bb\u03ae\u03c1\u03c9\u03c3\u03b7 \u03bc\u03bf\u03c4\u03af\u03b2\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03c4\u03bf in-context example \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bf\u03b4\u03b7\u03b3\u03af\u03b1. \u0397 \u03ba\u03b1\u03b9\u03bd\u03bf\u03c4\u03bf\u03bc\u03af\u03b1 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c4\u03cc\u03c3\u03bf \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03bf\u03c5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9.<\/p>\n<p>\u038c\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 forecast \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c0\u03b1\u03c1\u03b1\u03b3\u03b3\u03b5\u03bb\u03af\u03b5\u03c2, budget \u03ae \u03c3\u03c4\u03b5\u03bb\u03ad\u03c7\u03c9\u03c3\u03b7, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 audit trail. Query, retrieved example, normalization, \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1, post-processing \u03ba\u03b1\u03b9 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03c3\u03b5\u03b9\u03c1\u03ac \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1. \u03a7\u03c9\u03c1\u03af\u03c2 \u03b1\u03c5\u03c4\u03ac, \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03c3\u03b5\u03b9 \u03ba\u03b1\u03ba\u03ae \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b1 \u03b1\u03c0\u03cc generative \u03b1\u03c3\u03c4\u03bf\u03c7\u03af\u03b1 \u03ae \u03c3\u03c6\u03ac\u03bb\u03bc\u03b1 curve extraction.<\/p>\n<p>\u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 fallback \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae\u03c2 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7. \u0391\u03bd \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03ba\u03bf\u03bd\u03c4\u03b9\u03bd\u03cc \u03b9\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03cc \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03b1\u03bd \u03b7 \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c3\u03cd\u03bd\u03b4\u03b5\u03c4\u03b7 \u03ae \u03b1\u03bd \u03b7 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03b2\u03b3\u03b1\u03af\u03bd\u03b5\u03b9 \u03b5\u03ba\u03c4\u03cc\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ce\u03bd \u03bf\u03c1\u03af\u03c9\u03bd, \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03ad\u03c6\u03b5\u03b9 \u03c3\u03b5 \u03b1\u03c0\u03bb\u03bf\u03cd\u03c3\u03c4\u03b5\u03c1\u03bf baseline \u03ae \u03bd\u03b1 \u03b6\u03b7\u03c4\u03ac \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf. \u0397 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b1\u03bd\u03ac horizon, \u03c0\u03b5\u03c1\u03af\u03bf\u03b4\u03bf \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03ad\u03bf \u03ba\u03b1\u03b8\u03b5\u03c3\u03c4\u03ce\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd.<\/p>\n<p>\u03a4\u03bf \u03ce\u03c1\u03b9\u03bc\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03cc\u03bb\u03b5\u03c2 \u03bf\u03b9 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03af\u03bd\u03bf\u03c5\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1 foundation models \u03cc\u03c4\u03b1\u03bd \u03bc\u03b9\u03b1 \u03c0\u03b9\u03c3\u03c4\u03ae, \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03c1\u03ad\u03c8\u03b9\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ad\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03b9\u03ba\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2. \u0397 \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03bf backtest, shadow operation \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae \u03cc\u03c1\u03b9\u03b1 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">Predictive analytics \u03bc\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03b1\u03be\u03af\u03b1<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf pilot \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7\u03c2<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, baselines, \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2, failure modes \u03ba\u03b1\u03b9 KPI \u03c0\u03c1\u03b9\u03bd \u03ad\u03bd\u03b1 AI forecast \u03c3\u03c5\u03bd\u03b4\u03b5\u03b8\u03b5\u03af \u03bc\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03b3\u03b5\u03bb\u03af\u03b5\u03c2, \u03ba\u03b1\u03bc\u03c0\u03ac\u03bd\u03b9\u03b5\u03c2 \u03ae \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ad\u03c2 \u03c1\u03bf\u03ad\u03c2. \u0388\u03c4\u03c3\u03b9 \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc metric.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0394\u03b5\u03af\u03c4\u03b5 \u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u0395\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; AI<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">\u0427\u0435\u0441\u0442\u043e \u0437\u0430\u0434\u0430\u0432\u0430\u043d\u0438 \u0432\u044a\u043f\u0440\u043e\u0441\u0438<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf ICI-Time;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc framework \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03c3\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03b4\u03af\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03bf\u03c0\u03c4\u03b9\u03ba\u03cc input-output \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1 \u03c3\u03b5 \u03c0\u03c1\u03bf\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf vision model \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03ba\u03c4\u03ac \u03c4\u03b7\u03bd inpainted \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 fine-tuning \u03c4\u03bf\u03c5 vision model;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7. \u03a4\u03bf large vision model \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 off-the-shelf, \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b2\u03b1\u03c1\u03ce\u03bd \u03ae \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2, \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03ad\u03b3\u03b9\u03bd\u03b5 \u03bc\u03ad\u03c3\u03c9 visual in-context prompt.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03c4\u03bf ILI \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03b4\u03b7\u03bc\u03b9\u03bf\u03bb\u03bf\u03b3\u03af\u03b1, \u03c4\u03bf Weather \u03bc\u03b5 21 \u03bc\u03b5\u03c4\u03b5\u03c9\u03c1\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03bf\u03cd\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 ETT datasets \u03b1\u03c0\u03cc \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b5\u03c2 \u03b7\u03bb\u03b5\u03ba\u03c4\u03c1\u03b9\u03ba\u03ce\u03bd \u03bc\u03b5\u03c4\u03b1\u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03b9\u03c3\u03c4\u03ce\u03bd.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 full-data \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf ICI-Time \u03b5\u03af\u03c7\u03b5 \u03c4\u03b7\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 MAE \u03c3\u03b5 23 \u03b1\u03c0\u03cc 24 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 baselines. \u03a4\u03b1 MSE \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03ae\u03c4\u03b1\u03bd \u03c0\u03b9\u03bf \u03bc\u03b9\u03ba\u03c4\u03ac, \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03c3\u03c4\u03bf ETTh1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03c4\u03b1 few-shot \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c0\u03b9\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03b1\u03c0\u03cc Informer \u03ba\u03b1\u03b9 Pyraformer \u03cc\u03c4\u03b1\u03bd \u03c4\u03b1 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b1 training data \u03bc\u03b5\u03b9\u03ce\u03b8\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03bf 10%, 5% \u03ae 1% \u03c3\u03c4\u03b7\u03bd \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03ac\u03c4\u03b1\u03be\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03ac\u03bc\u03b5\u03c3\u03b1 \u03b3\u03b9\u03b1 e-commerce demand forecasting;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03ad\u03bf pilot. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b5 promotions, stockouts, calendar effects, price changes \u03ae retail \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 latency \u03ae \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc compute cost.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03cc \u03c1\u03af\u03c3\u03ba\u03bf;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc retrieval, \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b1, \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7, inpainting, curve extraction \u03ba\u03b1\u03b9 smoothing. \u0391\u03c3\u03c4\u03bf\u03c7\u03af\u03b1 \u03c3\u03b5 \u03bf\u03c0\u03bf\u03b9\u03bf\u03b4\u03ae\u03c0\u03bf\u03c4\u03b5 \u03c3\u03c4\u03ac\u03b4\u03b9\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03bf\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03c3\u03b5\u03b9\u03c1\u03ac.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc production \u03c7\u03c1\u03ae\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>Rolling backtest \u03c7\u03c9\u03c1\u03af\u03c2 leakage, \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03b5 \u03b1\u03c0\u03bb\u03ac \u03ba\u03b1\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ac baselines, business metrics, audit trail, shadow test, \u03cc\u03c1\u03b9\u03b1 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 fallback \u03b3\u03b9\u03b1 \u03b1\u03b2\u03ad\u03b2\u03b1\u03b9\u03b5\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03b9\u03c2.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">\u0418\u0437\u0442\u043e\u0447\u043d\u0438\u0446\u0438<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.23855\" target=\"_blank\" rel=\"noopener\">Nguyen et al. \u2014 In-Context Inpainting for Time Series Forecasting<\/a><\/li>\n<li><a href=\"https:\/\/www.bgc-jena.mpg.de\/wetter\/\" target=\"_blank\" rel=\"noopener\">Max Planck Institute for Biogeochemistry \u2014 Weather dataset<\/a><\/li>\n<li><a href=\"https:\/\/gis.cdc.gov\/grasp\/fluview\/fluportaldashboard.html\" target=\"_blank\" rel=\"noopener\">CDC FluView \u2014 \u0394\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 influenza-like illness<\/a><\/li>\n<li><a href=\"https:\/\/otexts.com\/fpp3\/tscv.html\" target=\"_blank\" rel=\"noopener\">Forecasting: Principles and Practice \u2014 Time series cross-validation<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST \u2014 AI Risk Management Framework<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf ICI-Time \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c7\u03c1\u03bf\u03bd\u03bf\u03c3\u03b5\u03b9\u03c1\u03ad\u03c2 \u03c3\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b5\u03af visual in-context learning \u03b3\u03b9\u03b1 forecasting. \u03a4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03c4\u03b1 few-shot \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03ad\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc pilot.<\/p>","protected":false},"author":1,"featured_media":105797,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[20815,7477,9529,20816,20817],"class_list":["post-98136","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ici-time","tag-machine-learning","tag-predictive-analytics","tag-time-series-forecasting","tag-vision-models"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/98136","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/comments?post=98136"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/98136\/revisions"}],"predecessor-version":[{"id":105798,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/98136\/revisions\/105798"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/105797"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=98136"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=98136"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=98136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}