{"id":106333,"date":"2026-09-17T07:33:24","date_gmt":"2026-09-16T23:33:24","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/106333.html"},"modified":"2026-09-17T07:33:24","modified_gmt":"2026-09-16T23:33:24","slug":"n-beats%ef%bc%88%e7%ba%af%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e6%a8%a1%e5%9e%8b%ef%bc%89%e4%bb%8b%e7%bb%8d","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/106333.html","title":{"rendered":"N-BEATS\uff08\u7eaf\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff09\u4ecb\u7ecd"},"content":{"rendered":"<p>N-BEATS&#xff08;Neural Basis Expansion Analysis for Interpretable Time Series Forecasting&#xff0c;\u53ef\u89e3\u91ca\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u7684\u795e\u7ecf\u57fa\u5c55\u5f00\u5206\u6790&#xff09;\u662f\u7531 Element AI \u548c\u8499\u7279\u5229\u5c14\u5b66\u4e60\u7b97\u6cd5\u7814\u7a76\u6240&#xff08;MILA&#xff09;\u7684\u7814\u7a76\u4eba\u5458\u4e8e 2019 \u5e74\u63d0\u51fa\u7684\u4e00\u79cd\u7eaf\u6df1\u5ea6\u5b66\u4e60\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u6a21\u578b\u3002<\/p>\n<p>\u5b83\u7684\u51fa\u73b0\u6311\u6218\u4e86\u5f53\u65f6\u201c\u6df1\u5ea6\u5b66\u4e60\u5fc5\u987b\u4e0e\u4f20\u7edf\u7edf\u8ba1\u65b9\u6cd5\u6df7\u5408\u624d\u80fd\u505a\u597d\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u201d\u7684\u666e\u904d\u8ba4\u77e5&#xff0c;\u8bc1\u660e\u4e86\u4ec5\u4f9d\u9760\u8db3\u591f\u6df1\u4e14\u5e72\u51c0\u7684\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784&#xff0c;\u5c31\u80fd\u5728\u5f02\u6784\u6570\u636e\u96c6\u4e0a\u8fbe\u5230\u751a\u81f3\u8d85\u8d8a\u4f20\u7edf\u7edf\u8ba1\u65b9\u6cd5\u548c\u7ade\u8d5b\u5f3a\u57fa\u7ebf\u7684\u6027\u80fd\u3002<\/p>\n<h4>1. \u6838\u5fc3\u67b6\u6784&#xff1a;\u7eaf\u524d\u9988\u4e0e\u53cc\u6b8b\u5dee\u5806\u53e0<\/h4>\n<p>\u4e0e\u4f9d\u8d56\u5faa\u73af\u7ed3\u6784&#xff08;\u5982 LSTM&#xff09;\u6216\u6ce8\u610f\u529b\u673a\u5236&#xff08;\u5982 Transformer&#xff09;\u7684\u6a21\u578b\u4e0d\u540c&#xff0c;N-BEATS \u662f\u4e00\u4e2a\u7eaf\u7cb9\u7684\u3001\u7aef\u5230\u7aef\u7684\u524d\u9988\u795e\u7ecf\u7f51\u7edc\u3002\u5176\u6838\u5fc3\u67b6\u6784\u5305\u542b\u4ee5\u4e0b\u5173\u952e\u8bbe\u8ba1&#xff1a;<\/p>\n<ul>\n<li>\u57fa\u7840\u6784\u5efa\u5757&#xff08;Basic Block&#xff09;&#xff1a;\u6bcf\u4e2a Block \u91c7\u7528\u201c\u53c9\u5f62\u201d\u7ed3\u6784&#xff0c;\u540c\u65f6\u8f93\u51fa\u4e24\u4e2a\u5411\u91cf&#xff1a;Backcast&#xff08;\u540e\u5411\u91cd\u5efa&#xff0c;\u7528\u4e8e\u62df\u5408\u5df2\u77e5\u5386\u53f2\u6570\u636e&#xff09;\u548c Forecast&#xff08;\u524d\u5411\u9884\u6d4b&#xff0c;\u7528\u4e8e\u751f\u6210\u672a\u6765\u9884\u6d4b\u503c&#xff09;\u3002<\/li>\n<li>\u53cc\u6b8b\u5dee\u5806\u53e0&#xff08;Doubly Residual Stacking, DRESS&#xff09;&#xff1a;\u8fd9\u662f N-BEATS \u6700\u5173\u952e\u7684\u62d3\u6251\u521b\u65b0\u3002\u5b83\u5305\u542b\u4e24\u6761\u72ec\u7acb\u7684\u6b8b\u5dee\u94fe\u8def&#xff1a;\u540e\u5411\u6b8b\u5dee\u94fe\u8def&#xff08;\u6bcf\u4e2a Block \u5c06 Backcast \u4ece\u8f93\u5165\u4e2d\u51cf\u53bb&#xff0c;\u4f7f\u540e\u7eed Block \u4ec5\u5904\u7406\u201c\u5269\u4f59\u4fe1\u53f7\u201d&#xff09;\u548c\u524d\u5411\u805a\u5408\u94fe\u8def&#xff08;\u5c06\u6bcf\u4e2a Block \u7684\u90e8\u5206\u9884\u6d4b\u9010\u5c42\u7d2f\u52a0&#xff0c;\u5f62\u6210\u5168\u5c40\u9884\u6d4b&#xff09;\u3002<\/li>\n<li>\u795e\u7ecf\u57fa\u5c55\u5f00&#xff08;Basis Expansion&#xff09;&#xff1a;\u6a21\u578b\u5185\u90e8\u901a\u8fc7\u53ef\u5b66\u4e60\u7684\u57fa\u51fd\u6570&#xff08;\u5982\u591a\u9879\u5f0f\u57fa\u3001\u5085\u91cc\u53f6\u57fa&#xff09;\u5c06\u4f4e\u7ef4\u53c2\u6570\u6620\u5c04\u4e3a\u5b8c\u6574\u7684\u65f6\u95f4\u8f68\u8ff9&#xff0c;\u4ece\u800c\u81ea\u52a8\u903c\u8fd1\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u6f5c\u5728\u7ed3\u6784\u3002<\/li>\n<\/ul>\n<h4>2. \u6a21\u578b\u914d\u7f6e\u4e0e\u53ef\u89e3\u91ca\u6027<\/h4>\n<p>N-BEATS \u63d0\u4f9b\u4e86\u4e24\u79cd\u4e3b\u8981\u914d\u7f6e&#xff0c;\u5728\u7cbe\u5ea6\u4e0e\u53ef\u89e3\u91ca\u6027\u4e4b\u95f4\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u9009\u62e9&#xff1a;<\/p>\n<ul>\n<li>\u901a\u7528\u6a21\u5f0f&#xff08;Generic Block&#xff09;&#xff1a;\u4e0d\u52a0\u5165\u4efb\u4f55\u5173\u4e8e\u65f6\u95f4\u5e8f\u5217\u7684\u7ed3\u6784\u5047\u8bbe&#xff0c;\u76f4\u63a5\u5229\u7528\u5168\u8fde\u63a5\u5c42\u5b66\u4e60\u590d\u6742\u7684\u975e\u7ebf\u6027\u6a21\u5f0f&#xff0c;\u8ffd\u6c42\u6781\u81f4\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002<\/li>\n<li>\u53ef\u89e3\u91ca\u6a21\u5f0f&#xff08;Interpretable Block&#xff09;&#xff1a;\u5c06\u8d8b\u52bf&#xff08;Trend&#xff09;\u548c\u5b63\u8282\u6027&#xff08;Seasonality&#xff09;\u5efa\u6a21\u4e3a\u53ef\u89e3\u91ca\u7684\u5f62\u5f0f&#xff08;\u5982\u4f7f\u7528\u591a\u9879\u5f0f\u57fa\u62df\u5408\u8d8b\u52bf&#xff0c;\u5085\u91cc\u53f6\u57fa\u6355\u6349\u5468\u671f\u6027&#xff09;\u3002\u5728\u8fd9\u79cd\u6a21\u5f0f\u4e0b&#xff0c;\u6a21\u578b\u53ef\u4ee5\u50cf\u4f20\u7edf STL \u5206\u89e3\u4e00\u6837&#xff0c;\u6e05\u6670\u5730\u53ef\u89c6\u5316\u51fa\u6bcf\u4e2a Block \u8d21\u732e\u7684\u8d8b\u52bf\u66f2\u7ebf\u4e0e\u5468\u671f\u6210\u5206&#xff0c;\u6253\u7834\u4e86\u6df1\u5ea6\u5b66\u4e60\u201c\u9ed1\u76d2\u201d\u7684\u523b\u677f\u5370\u8c61\u3002<\/li>\n<\/ul>\n<h4>3. \u6838\u5fc3\u4f18\u52bf<\/h4>\n<ul>\n<li>\u65e0\u9700\u7279\u5f81\u5de5\u7a0b&#xff1a;\u5b8c\u5168\u57fa\u4e8e\u539f\u59cb\u65f6\u95f4\u5e8f\u5217\u8f93\u5165&#xff0c;\u4ec5\u9700\u6ed1\u52a8\u7a97\u53e3\u5207\u5206&#xff0c;\u7701\u53bb\u4e86\u5dee\u5206\u3001\u5e73\u7a33\u5316\u7b49\u7e41\u7410\u7684\u4f20\u7edf\u9884\u5904\u7406\u6b65\u9aa4\u3002<\/li>\n<li>\u5f3a\u6cdb\u5316\u80fd\u529b&#xff1a;\u5728 M3\u3001M4 \u548c TOURISM \u7b49\u77e5\u540d\u7ade\u8d5b\u6570\u636e\u96c6\u4e0a&#xff0c;N-BEATS \u5c55\u73b0\u4e86\u5f3a\u5927\u7684\u8de8\u9886\u57df\u6cdb\u5316\u80fd\u529b&#xff0c;\u65e0\u9700\u9488\u5bf9\u7279\u5b9a\u9891\u7387\u624b\u5de5\u8bbe\u8ba1\u67b6\u6784\u5373\u53ef\u53d6\u5f97 SOTA \u6027\u80fd\u3002<\/li>\n<li>\u6613\u4e8e\u5e76\u884c\u8bad\u7ec3&#xff1a;\u7531\u4e8e\u5b8c\u5168\u57fa\u4e8e\u5168\u8fde\u63a5\u5c42\u4e14\u65e0\u65f6\u5e8f\u9012\u5f52\u4f9d\u8d56&#xff0c;\u5176\u6279\u5904\u7406\u6548\u7387\u8fdc\u8d85 RNN \u67b6\u6784&#xff0c;\u8bad\u7ec3\u901f\u5ea6\u6781\u5feb\u3002<\/li>\n<\/ul>\n<h4>4. \u5c40\u9650\u6027\u4e0e\u9650\u5236<\/h4>\n<ul>\n<li>\u4e0d\u652f\u6301\u5916\u90e8\u534f\u53d8\u91cf&#xff1a;\u539f\u59cb\u7248\u672c\u7684 N-BEATS \u4ec5\u652f\u6301\u5355\u53d8\u91cf\u65f6\u95f4\u5e8f\u5217&#xff0c;\u65e0\u6cd5\u878d\u5408\u5982\u4fc3\u9500\u3001\u5929\u6c14\u3001\u4ef7\u683c\u7b49\u5916\u90e8\u534f\u53d8\u91cf&#xff08;Exogenous features&#xff09;\u3002&#xff08;\u6ce8&#xff1a;\u540e\u7eed\u5b66\u672f\u754c\u63d0\u51fa\u4e86 N-BEATSx \u6269\u5c55\u7248\u672c\u4ee5\u89e3\u51b3\u6b64\u95ee\u9898&#xff09;\u3002<\/li>\n<li>\u8bad\u7ec3\u6210\u672c\u4e0e\u8d85\u53c2\u6570\u654f\u611f&#xff1a;\u4f5c\u4e3a\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc&#xff0c;\u5176\u8bad\u7ec3\u8017\u65f6\u548c\u786c\u4ef6\u8981\u6c42\u9ad8\u4e8e\u4f20\u7edf\u7edf\u8ba1\u6a21\u578b&#xff1b;\u540c\u65f6&#xff0c;Block \u7684\u5806\u53e0\u65b9\u5f0f\u3001\u9690\u85cf\u5c42\u5bbd\u5ea6\u7b49\u8d85\u53c2\u6570\u5bf9\u6700\u7ec8\u6548\u679c\u6709\u8f83\u5927\u5f71\u54cd\u3002<\/li>\n<li>\u90e8\u5206\u6a21\u5f0f\u7f3a\u4e4f\u89e3\u91ca\u6027&#xff1a;\u5982\u679c\u4f7f\u7528 Generic Block&#xff0c;\u6a21\u578b\u4f9d\u7136\u662f\u4e00\u4e2a\u9ed1\u76d2&#xff0c;\u96be\u4ee5\u5411\u4e1a\u52a1\u65b9\u89e3\u91ca\u5177\u4f53\u7684\u9a71\u52a8\u56e0\u7d20\u3002<\/li>\n<\/ul>\n<h4>5. \u5178\u578b\u5e94\u7528\u573a\u666f<\/h4>\n<p>N-BEATS \u5c24\u5176\u9002\u5408\u6570\u636e\u91cf\u5927\u3001\u9884\u6d4b\u7a97\u53e3\u7075\u6d3b\u4e14\u96be\u4ee5\u6784\u9020\u989d\u5916\u7279\u5f81\u7684\u573a\u666f&#xff1a;<\/p>\n<ul>\n<li>\u96f6\u552e\u4e0e\u7535\u5546&#xff1a;\u6210\u5343\u4e0a\u4e07\u4e2a SKU \u7684\u9500\u91cf\u9884\u6d4b\u3001\u5e93\u5b58\u7ba1\u7406\u3002<\/li>\n<li>\u80fd\u6e90\u4e0e\u7535\u529b&#xff1a;\u7535\u529b\u8d1f\u8377\u9884\u6d4b\u3001\u80fd\u6e90\u6d88\u8017\u5206\u6790\u3002<\/li>\n<li>\u91d1\u878d\u5e02\u573a&#xff1a;\u8d44\u4ea7\u4ef7\u683c\u8d70\u52bf\u9884\u6d4b\u3002<\/li>\n<li>IT \u8fd0\u7ef4&#xff1a;Web \u6d41\u91cf\u3001\u4e91\u670d\u52a1\u7528\u6237\u6d3b\u8dc3\u5ea6\u9884\u6d4b\u3002<\/li>\n<\/ul>\n<h4>6. \u5f00\u6e90\u4e0e\u5de5\u7a0b\u5b9e\u73b0<\/h4>\n<p>N-BEATS \u62e5\u6709\u975e\u5e38\u6210\u719f\u7684\u5f00\u6e90\u751f\u6001&#xff0c;\u5f00\u53d1\u8005\u53ef\u4ee5\u901a\u8fc7\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u5feb\u901f\u8c03\u7528\u3002\u4f8b\u5982&#xff0c;\u5728 Amazon \u5f00\u6e90\u7684 GluonTS \u5e93\u4e2d\u63d0\u4f9b\u4e86\u00a0NBEATSEstimator&#xff0c;\u540c\u65f6\u793e\u533a\u4e5f\u6709\u5927\u91cf\u57fa\u4e8e PyTorch \u548c TensorFlow \u7684\u5b98\u65b9\u53ca\u7b2c\u4e09\u65b9\u5b9e\u73b0&#xff0c;\u65b9\u4fbf\u8fdb\u884c\u5feb\u901f\u5b9e\u9a8c\u4e0e\u5de5\u4e1a\u7ea7\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>N-BEATS&#xff08;Neural Basis Expansion Analysis for Interpretable Time Series Forecasting&#xff0c;\u53ef\u89e3\u91ca\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u7684\u795e\u7ecf\u57fa\u5c55\u5f00\u5206\u6790&#xff09;\u662f\u7531 Element AI \u548c\u8499\u7279\u5229\u5c14\u5b66\u4e60\u7b97\u6cd5\u7814\u7a76\u6240&#xff08;MILA&#xff09;\u7684\u7814\u7a76\u4eba\u5458\u4e8e 2019 \u5e74\u63d0\u51fa\u7684\u4e00\u79cd\u7eaf\u6df1\u5ea6\u5b66\u4e60\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u6a21\u578b\u3002\u5b83\u7684\u51fa\u73b0\u6311\u6218\u4e86\u5f53\u65f6\u201c\u6df1\u5ea6\u5b66\u4e60\u5fc5\u987b\u4e0e\u4f20\u7edf\u7edf\u8ba1\u65b9\u6cd5\u6df7\u5408\u624d\u80fd\u505a\u597d\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u201d\u7684\u666e\u904d\u8ba4\u77e5<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,86],"topic":[],"class_list":["post-106333","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-86"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>N-BEATS\uff08\u7eaf\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff09\u4ecb\u7ecd - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/106333.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"N-BEATS\uff08\u7eaf\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff09\u4ecb\u7ecd - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"N-BEATS&#xff08;Neural Basis Expansion Analysis for Interpretable Time Series Forecasting&#xff0c;\u53ef\u89e3\u91ca\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u7684\u795e\u7ecf\u57fa\u5c55\u5f00\u5206\u6790&#xff09;\u662f\u7531 Element AI \u548c\u8499\u7279\u5229\u5c14\u5b66\u4e60\u7b97\u6cd5\u7814\u7a76\u6240&#xff08;MILA&#xff09;\u7684\u7814\u7a76\u4eba\u5458\u4e8e 2019 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