{"id":879,"date":"2023-07-28T11:11:00","date_gmt":"2023-07-28T11:11:00","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e8%87%aa%e7%9b%b8%e5%85%b3-python\/"},"modified":"2023-07-28T11:11:00","modified_gmt":"2023-07-28T11:11:00","slug":"%e8%87%aa%e7%9b%b8%e5%85%b3-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e8%87%aa%e7%9b%b8%e5%85%b3-python\/","title":{"rendered":"\u5982\u4f55\u7528 python \u8ba1\u7b97\u81ea\u76f8\u5173"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u81ea\u76f8\u5173\u8861\u91cf<\/strong>\u8fde\u7eed\u65f6\u95f4\u95f4\u9694\u5185\u65f6\u95f4\u5e8f\u5217\u4e0e\u5176\u81ea\u8eab\u6ede\u540e\u7248\u672c\u4e4b\u95f4\u7684\u76f8\u4f3c\u7a0b\u5ea6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6709\u65f6\u4e5f\u79f0\u4e3a\u201c\u5e8f\u5217\u76f8\u5173\u201d\u6216\u201c\u6ede\u540e\u76f8\u5173\u201d\uff0c\u56e0\u4e3a\u5b83\u8861\u91cf\u53d8\u91cf\u7684\u5f53\u524d\u503c\u4e0e\u5176\u5386\u53f2\u503c\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f53\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u81ea\u76f8\u5173\u6027\u8f83\u9ad8\u65f6\uff0c\u53ea\u9700\u53c2\u8003\u8fc7\u53bb\u7684\u503c\u5373\u53ef\u8f7b\u677e\u9884\u6d4b\u672a\u6765\u503c\u3002<\/span><\/p>\n<h3><strong>\u5982\u4f55\u7528 Python \u8ba1\u7b97\u81ea\u76f8\u5173<\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5728 Python \u4e2d\u6709\u4ee5\u4e0b\u65f6\u95f4\u5e8f\u5217\uff0c\u663e\u793a 15 \u4e2a\u4e0d\u540c\u65f6\u671f\u5185\u67d0\u4e2a\u53d8\u91cf\u7684\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data<\/span>\nx = [22, 24, 25, 25, 28, 29, 34, 37, 40, 44, 51, 48, 47, 50, 51]\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 statsmodels \u5e93\u4e2d\u7684<a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.tsa.stattools.acf.html\" target=\"_blank\" rel=\"noopener noreferrer\">acf() \u51fd\u6570<\/a>\u8ba1\u7b97\u65f6\u95f4\u5e8f\u5217\u4e2d\u6bcf\u4e2a\u6ede\u540e\u7684\u81ea\u76f8\u5173\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> statsmodels.api <span style=\"color: #107d3f;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#calculate autocorrelations<\/span>\nsm.tsa.acf(x)\n\narray([ 1. , 0.83174224, 0.65632458, 0.49105012, 0.27863962,\n        0.03102625, -0.16527446, -0.30369928, -0.40095465, -0.45823389,\n       -0.45047733])\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7ed3\u679c\u89e3\u91ca\u65b9\u5f0f\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6ede\u540e 0 \u5904\u7684\u81ea\u76f8\u5173\u4e3a<strong>1<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6ede\u540e 1 \u5904\u7684\u81ea\u76f8\u5173\u4e3a<strong>0.8317<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6ede\u540e 2 \u5904\u7684\u81ea\u76f8\u5173\u4e3a<strong>0.6563<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6ede\u540e 3 \u5904\u7684\u81ea\u76f8\u5173\u4e3a<strong>0.4910<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7<strong>nlags<\/strong>\u53c2\u6570\u6307\u5b9a\u8981\u4f7f\u7528\u7684\u6ede\u540e\u6570\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>sm.tsa.acf(x, nlags= <span style=\"color: #008000;\">5<\/span> )\n\narray([1.0, 0.83174224, 0.65632458, 0.49105012, 0.27863962, 0.03102625])<\/strong><\/pre>\n<h3><strong>\u5982\u4f55\u5728Python\u4e2d\u7ed8\u5236\u81ea\u76f8\u5173\u51fd\u6570<\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 statsmodels \u5e93\u4e2d\u7684<a href=\"https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.graphics.tsaplots.plot_acf.html\" target=\"_blank\" rel=\"noopener noreferrer\">tsaplots.plot_acf() \u51fd\u6570<\/a>\u5728 Python \u4e2d\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u7684\u81ea\u76f8\u5173\u51fd\u6570\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> statsmodels.graphics <span style=\"color: #107d3f;\">import<\/span> tsaplots\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot autocorrelation function<\/span>\nfig = tsaplots.plot_acf(x, lags=10)\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9480 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython1.png\" alt=\"Python\u4e2d\u7684\u81ea\u76f8\u5173\u51fd\u6570\" width=\"495\" height=\"343\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">x \u8f74\u663e\u793a\u6ede\u540e\u6570\uff0cy \u8f74\u663e\u793a\u8be5\u6ede\u540e\u6570\u5904\u7684\u81ea\u76f8\u5173\u3002\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u7ed8\u56fe\u4ece lag = 0 \u5f00\u59cb\uff0c\u5e76\u4e14\u5728 lag = 0 \u65f6\u81ea\u76f8\u5173\u59cb\u7ec8\u4e3a<strong>1<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u9009\u62e9\u4f7f\u7528\u8f83\u5c11\u7684\u6ede\u540e\u4e0e<strong>lags<\/strong>\u53c2\u6570\u6765\u653e\u5927\u7b2c\u4e00\u4e2a\u6ede\u540e\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> statsmodels.graphics <span style=\"color: #107d3f;\">import<\/span> tsaplots\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot autocorrelation function<\/span>\nfig = tsaplots.plot_acf(x, lags= <span style=\"color: #008000;\">5<\/span> )\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9481 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython2.png\" alt=\"\u5728 Python \u4e2d\u7ed8\u5236\u81ea\u76f8\u5173\u51fd\u6570\" width=\"495\" height=\"329\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528\u6807\u9898\u548c\u989c\u8272\u53c2\u6570\u66f4\u6539\u56fe<strong>\u4e2d<\/strong>\u4f7f\u7528\u7684\u5706\u5708\u7684\u6807\u9898\u548c<strong>\u989c\u8272<\/strong>\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> statsmodels.graphics <span style=\"color: #107d3f;\">import<\/span> tsaplots\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot autocorrelation function<\/span>\nfig = tsaplots.plot_acf(x, lags= <span style=\"color: #008000;\"><span style=\"color: #000000;\">5, color='g', title='Autocorrelation function'<\/span><\/span> <span style=\"color: #000000;\">)<\/span>\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9482 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython4.png\" alt=\"Python \u4e2d\u5177\u6709\u81ea\u5b9a\u4e49\u6807\u9898\u7684\u81ea\u76f8\u5173\u51fd\u6570\" width=\"499\" height=\"342\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\"><em>\u60a8\u53ef\u4ee5\u5728<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u5b66\u4ee5\u7b80\u5355\u76f4\u63a5\u7684\u65b9\u5f0f\u89e3\u91ca\u6982\u5ff5\uff0c\u6211\u4eec\u4f7f\u5b66\u4e60\u7edf\u8ba1\u53d8\u5f97\u66f4\u5bb9\u6613\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u6b64\u9875\u9762<\/a>\u4e0a\u627e\u5230\u66f4\u591a Python \u6559\u7a0b\u3002<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u81ea\u76f8\u5173\u8861\u91cf\u8fde\u7eed\u65f6\u95f4\u95f4\u9694\u5185\u65f6\u95f4\u5e8f\u5217\u4e0e\u5176\u81ea\u8eab\u6ede\u540e\u7248\u672c\u4e4b\u95f4\u7684\u76f8\u4f3c\u7a0b\u5ea6\u3002 \u6709\u65f6\u4e5f\u79f0\u4e3a\u201c\u5e8f\u5217\u76f8\u5173\u201d\u6216\u201c\u6ede\u540e\u76f8\u5173\u201d\uff0c\u56e0\u4e3a\u5b83 [&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-879","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>\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u81ea\u76f8\u5173 - \u7edf\u8ba1\u5b66<\/title>\n<meta name=\"description\" content=\"\u5173\u4e8e\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u548c\u7ed8\u5236\u81ea\u76f8\u5173\u51fd\u6570\u7684\u7b80\u5355\u8bf4\u660e\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\/\u81ea\u76f8\u5173-python\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta 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