{"id":1760,"date":"2023-07-25T02:36:28","date_gmt":"2023-07-25T02:36:28","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e8%bf%aa%e5%9f%ba%c2%b7%e5%af%8c%e5%8b%92%e6%b5%8b%e8%af%95-python\/"},"modified":"2023-07-25T02:36:28","modified_gmt":"2023-07-25T02:36:28","slug":"%e8%bf%aa%e5%9f%ba%c2%b7%e5%af%8c%e5%8b%92%e6%b5%8b%e8%af%95-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e8%bf%aa%e5%9f%ba%c2%b7%e5%af%8c%e5%8b%92%e6%b5%8b%e8%af%95-python\/","title":{"rendered":"Python \u4e2d\u7684\u589e\u5f3a dickey-fuller \u6d4b\u8bd5\uff08\u5e26\u6709\u793a\u4f8b\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5982\u679c\u65f6\u95f4\u5e8f\u5217\u6ca1\u6709\u8d8b\u52bf\u3001\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u5448\u73b0\u6052\u5b9a\u7684\u65b9\u5dee\u5e76\u4e14\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u5177\u6709\u6052\u5b9a\u7684\u81ea\u76f8\u5173\u7ed3\u6784\uff0c\u5219\u8be5\u65f6\u95f4\u5e8f\u5217\u88ab\u79f0\u4e3a\u201c\u5e73\u7a33\u201d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6d4b\u8bd5\u65f6\u95f4\u5e8f\u5217\u662f\u5426\u5e73\u7a33\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u6267\u884c<strong>\u589e\u5f3a\u8fea\u57fa-\u5bcc\u52d2\u68c0\u9a8c<\/strong>\uff0c\u8be5\u68c0\u9a8c\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub> \uff1a<\/strong>\u65f6\u95f4\u5e8f\u5217\u662f\u975e\u5e73\u7a33\u7684\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5b83\u7684\u7ed3\u6784\u53d6\u51b3\u4e8e\u65f6\u95f4\uff0c\u5e76\u4e14\u5b83\u7684\u53d8\u5316\u5e76\u4e0d\u968f\u65f6\u95f4\u6052\u5b9a\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub> \uff1a<\/strong>\u65f6\u95f4\u5e8f\u5217\u662f\u5e73\u7a33\u7684\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u68c0\u9a8c\u7684<a href=\"https:\/\/statorials.org\/cn\/p\u503c\u7edf\u8ba1\u663e\u7740\u6027\/\" target=\"_blank\" rel=\"noopener\">p \u503c<\/a>\u4f4e\u4e8e\u4e00\u5b9a\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u4f8b\u5982 \u03b1 = 0.05\uff09\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u62d2\u7edd\u539f\u5047\u8bbe\u5e76\u5f97\u51fa\u65f6\u95f4\u5e8f\u5217\u5e73\u7a33\u7684\u7ed3\u8bba\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u6f14\u793a\u5982\u4f55\u5728 Python \u4e2d\u9488\u5bf9\u7ed9\u5b9a\u65f6\u95f4\u5e8f\u5217\u6267\u884c\u589e\u5f3a\u7684 Dickey-Fuller \u6d4b\u8bd5\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aPython \u4e2d\u7684\u589e\u5f3a Dickey-Fuller \u6d4b\u8bd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5728Python\u4e2d\u6709\u4ee5\u4e0b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>data = [3, 4, 4, 5, 6, 7, 6, 6, 7, 8, 9, 12, 10]<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5728\u5bf9\u6570\u636e\u6267\u884c\u589e\u5f3a\u7684 Dickey-Fuller \u6d4b\u8bd5\u4e4b\u524d\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5feb\u901f\u7ed8\u56fe\u6765\u53ef\u89c6\u5316\u6570\u636e\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\nplt. <span style=\"color: #3366ff;\">plot<\/span> (data)<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-17233 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/adf1.png\" alt=\"\" width=\"400\" height=\"275\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u8981\u6267\u884c\u589e\u5f3a\u7684 Dickey-Fuller \u6d4b\u8bd5\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>statsmodels<\/strong>\u5e93\u4e2d\u7684<a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.tsa.stattools.adfuller.html\" target=\"_blank\" rel=\"noopener\">adfuller()<\/a>\u51fd\u6570\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5 statsmodels\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>pip install statsmodels\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u6267\u884c\u589e\u5f3a\u7684Dickey-Fuller\u6d4b\u8bd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> statsmodels. <span style=\"color: #3366ff;\">tsa<\/span> . <span style=\"color: #3366ff;\">stattools<\/span> <span style=\"color: #008000;\">import<\/span> adfuller\n\n<span style=\"color: #008080;\">#perform augmented Dickey-Fuller test<\/span>\nadfuller(data)\n\n(-0.9753836234744063,\n 0.7621363564361013,\n 0,\n 12,\n {'1%': -4.137829282407408,\n  '5%': -3.1549724074074077,\n  '10%': -2.7144769444444443},\n 31.2466098872313)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\u4e2d\u6700\u91cd\u8981\u7684\u503c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u68c0\u9a8c\u7edf\u8ba1\u91cf\uff1a <strong>-0.97538<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">P \u503c\uff1a <strong>0.7621<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7531\u4e8e p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u65e0\u6cd5\u62d2\u7edd\u539f\u5047\u8bbe\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u610f\u5473\u7740\u65f6\u95f4\u5e8f\u5217\u4e0d\u662f\u5e73\u7a33\u7684\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5b83\u7684\u7ed3\u6784\u53d6\u51b3\u4e8e\u65f6\u95f4\uff0c\u5e76\u4e14\u5b83\u7684\u53d8\u5316\u5e76\u4e0d\u968f\u65f6\u95f4\u6052\u5b9a\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/cn\/matplotlib-\u65f6\u95f4\u5e8f\u5217\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Matplotlib \u4e2d\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/pandas-\u591a\u60c5\u8282\u7cfb\u5217\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u4ece Pandas DataFrame \u7ed8\u5236\u591a\u4e2a\u7cfb\u5217<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u66fc\u80af\u5fb7\u5c14\u6d4b\u8bd5python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u4f7f\u7528 Python \u6267\u884c Mann-Kendall \u8d8b\u52bf\u68c0\u9a8c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5982\u679c\u65f6\u95f4\u5e8f\u5217\u6ca1\u6709\u8d8b\u52bf\u3001\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u5448\u73b0\u6052\u5b9a\u7684\u65b9\u5dee\u5e76\u4e14\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u5177\u6709\u6052\u5b9a\u7684\u81ea\u76f8\u5173\u7ed3\u6784\uff0c\u5219\u8be5\u65f6\u95f4\u5e8f\u5217\u88ab\u79f0\u4e3a\u201c [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1760","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u4e2d\u7684\u589e\u5f3a Dickey-Fuller \u6d4b\u8bd5\uff08\u5e26\u6709\u793a\u4f8b\uff09<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u589e\u5f3a\u7684 Dickey-Fuller \u6d4b\u8bd5\uff0c\u5305\u62ec\u5206\u6b65\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/\u8fea\u57fa\u00b7\u5bcc\u52d2\u6d4b\u8bd5-python\/\" 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