{"id":935576,"date":"2024-12-26T18:56:06","date_gmt":"2024-12-26T10:56:06","guid":{"rendered":""},"modified":"2024-12-26T18:56:08","modified_gmt":"2024-12-26T10:56:08","slug":"python%e5%a6%82%e4%bd%95%e5%8e%bb%e8%b6%8b%e5%8a%bf","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/935576.html","title":{"rendered":"python\u5982\u4f55\u53bb\u8d8b\u52bf"},"content":{"rendered":"<p style=\"text-align:center;\" ><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25072138\/e7209c26-1cf9-405e-a283-e983928d809c.webp\" alt=\"python\u5982\u4f55\u53bb\u8d8b\u52bf\" \/><\/p>\n<p><p> <strong>\u5728Python\u4e2d\u53bb\u9664\u8d8b\u52bf\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u5dee\u5206\u6cd5\u3001\u5e94\u7528HP\u6ee4\u6ce2\u5668\u3001\u5229\u7528\u79fb\u52a8\u5e73\u5747\u6cd5\u3002\u5dee\u5206\u6cd5\u662f\u6700\u76f4\u63a5\u7684\u65b9\u6cd5\uff0c\u901a\u8fc7\u8ba1\u7b97\u76f8\u90bb\u6570\u636e\u7684\u5dee\u503c\u6765\u6d88\u9664\u8d8b\u52bf\uff1bHP\u6ee4\u6ce2\u5668\u662f\u4e00\u79cd\u5e73\u6ed1\u6280\u672f\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u5206\u79bb\u51fa\u8d8b\u52bf\u6210\u5206\uff1b\u79fb\u52a8\u5e73\u5747\u6cd5\u901a\u8fc7\u5e73\u6ed1\u6570\u636e\u6765\u51cf\u5c11\u8d8b\u52bf\u7684\u5f71\u54cd\u3002<\/strong><\/p>\n<\/p>\n<p><p>\u53bb\u9664\u8d8b\u52bf\u662f\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\u3002\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5e38\u5e38\u5305\u542b\u8d8b\u52bf\u3001\u5b63\u8282\u6027\u3001\u5468\u671f\u6027\u548c\u968f\u673a\u6027\u7b49\u6210\u5206\u3002\u53bb\u9664\u8d8b\u52bf\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u8bc6\u522b\u548c\u5efa\u6a21\u5176\u4ed6\u6210\u5206\uff0c\u7279\u522b\u662f\u5b63\u8282\u6027\u548c\u968f\u673a\u6027\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u51e0\u79cd\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<p><p>\u4e00\u3001\u5dee\u5206\u6cd5<\/p>\n<\/p>\n<p><p>\u5dee\u5206\u6cd5\u662f\u53bb\u9664\u8d8b\u52bf\u7684\u5e38\u7528\u65b9\u6cd5\u4e4b\u4e00\u3002\u901a\u8fc7\u8ba1\u7b97\u65f6\u95f4\u5e8f\u5217\u4e2d\u76f8\u90bb\u6570\u636e\u70b9\u7684\u5dee\u503c\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u53bb\u9664\u7ebf\u6027\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u5dee\u5206\u6cd5\u7684\u57fa\u672c\u539f\u7406<\/strong><\/p>\n<\/p>\n<p><p>\u5dee\u5206\u6cd5\u901a\u8fc7\u8ba1\u7b97\u65f6\u95f4\u5e8f\u5217\u4e2d\u76f8\u90bb\u70b9\u7684\u5dee\u503c\uff0c\u4ee5\u53bb\u9664\u5176\u4e2d\u7684\u8d8b\u52bf\u6210\u5206\u3002\u5bf9\u4e8e\u4e00\u4e2a\u7ed9\u5b9a\u7684\u65f6\u95f4\u5e8f\u5217 (X_t)\uff0c\u5176\u5dee\u5206\u5e8f\u5217\u53ef\u4ee5\u8868\u793a\u4e3a (Y_t = X_t &#8211; X_{t-1})\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5e94\u7528\u5dee\u5206\u6cd5\u7684\u6b65\u9aa4<\/strong><\/p>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u5dee\u5206\u6cd5\u53ef\u4ee5\u901a\u8fc7pandas\u5e93\u6765\u5b9e\u73b0\u3002\u9996\u5148\uff0c\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u7136\u540e\u901a\u8fc7<code>pandas<\/code>\u4e2d\u7684<code>diff<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5dee\u5206\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n<p>data = pd.Series([1, 2, 4, 7, 11, 16, 22])<\/p>\n<h2><strong>\u8ba1\u7b97\u5dee\u5206<\/strong><\/h2>\n<p>differenced_data = data.diff().dropna()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5dee\u5206\u6cd5\u7684\u4f18\u7f3a\u70b9<\/strong><\/p>\n<\/p>\n<p><p><strong>\u4f18\u70b9<\/strong>\uff1a\u5dee\u5206\u6cd5\u7b80\u5355\u6613\u884c\uff0c\u9002\u7528\u4e8e\u7ebf\u6027\u8d8b\u52bf\u7684\u53bb\u9664\u3002<\/p>\n<\/p>\n<p><p><strong>\u7f3a\u70b9<\/strong>\uff1a\u5bf9\u4e8e\u975e\u7ebf\u6027\u8d8b\u52bf\uff0c\u5dee\u5206\u6cd5\u53ef\u80fd\u4e0d\u591f\u6709\u6548\u3002\u6b64\u5916\uff0c\u5dee\u5206\u4f1a\u589e\u52a0\u6570\u636e\u7684\u6ce2\u52a8\u6027\uff0c\u9700\u8981\u5c0f\u5fc3\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e8c\u3001HP\u6ee4\u6ce2\u5668<\/p>\n<\/p>\n<p><p>HP\u6ee4\u6ce2\u5668\uff08Hodrick-Prescott\u6ee4\u6ce2\u5668\uff09\u662f\u4e00\u79cd\u7528\u4e8e\u5206\u89e3\u65f6\u95f4\u5e8f\u5217\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5c06\u5e8f\u5217\u5206\u4e3a\u8d8b\u52bf\u548c\u5468\u671f\u6027\u6210\u5206\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>HP\u6ee4\u6ce2\u5668\u7684\u57fa\u672c\u539f\u7406<\/strong><\/p>\n<\/p>\n<p><p>HP\u6ee4\u6ce2\u5668\u901a\u8fc7\u6700\u5c0f\u5316\u4e00\u4e2a\u76ee\u6807\u51fd\u6570\u6765\u5206\u89e3\u65f6\u95f4\u5e8f\u5217\u3002\u76ee\u6807\u51fd\u6570\u7531\u8d8b\u52bf\u6210\u5206\u7684\u5e73\u6ed1\u6027\u548c\u4e0e\u5b9e\u9645\u6570\u636e\u7684\u62df\u5408\u5ea6\u7ec4\u6210\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5e94\u7528HP\u6ee4\u6ce2\u5668\u7684\u6b65\u9aa4<\/strong><\/p>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528<code>statsmodels<\/code>\u5e93\u4e2d\u7684<code>hpfilter<\/code>\u65b9\u6cd5\u6765\u5e94\u7528HP\u6ee4\u6ce2\u5668\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<p>from statsmodels.tsa.filters.hp_filter import hpfilter<\/p>\n<h2><strong>\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n<p>data = pd.Series([1, 2, 4, 7, 11, 16, 22])<\/p>\n<h2><strong>\u5e94\u7528HP\u6ee4\u6ce2\u5668<\/strong><\/h2>\n<p>cycle, trend = hpfilter(data, lamb=1600)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>HP\u6ee4\u6ce2\u5668\u7684\u4f18\u7f3a\u70b9<\/strong><\/p>\n<\/p>\n<p><p><strong>\u4f18\u70b9<\/strong>\uff1aHP\u6ee4\u6ce2\u5668\u53ef\u4ee5\u6709\u6548\u5206\u79bb\u8d8b\u52bf\u548c\u5468\u671f\u6210\u5206\uff0c\u9002\u7528\u4e8e\u975e\u7ebf\u6027\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<p><p><strong>\u7f3a\u70b9<\/strong>\uff1a\u9700\u8981\u9009\u62e9\u5408\u9002\u7684\u5e73\u6ed1\u53c2\u6570\uff08lambda\uff09\uff0c\u4e0d\u540c\u7684\u53c2\u6570\u53ef\u80fd\u4f1a\u4ea7\u751f\u4e0d\u540c\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u4e09\u3001\u79fb\u52a8\u5e73\u5747\u6cd5<\/p>\n<\/p>\n<p><p>\u79fb\u52a8\u5e73\u5747\u6cd5\u901a\u8fc7\u8ba1\u7b97\u65f6\u95f4\u5e8f\u5217\u7684\u5e73\u5747\u503c\u6765\u5e73\u6ed1\u6570\u636e\uff0c\u4ece\u800c\u53bb\u9664\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u79fb\u52a8\u5e73\u5747\u6cd5\u7684\u57fa\u672c\u539f\u7406<\/strong><\/p>\n<\/p>\n<p><p>\u79fb\u52a8\u5e73\u5747\u6cd5\u901a\u8fc7\u8ba1\u7b97\u65f6\u95f4\u5e8f\u5217\u4e2d\u4e00\u5b9a\u7a97\u53e3\u5927\u5c0f\u7684\u5e73\u5747\u503c\u6765\u5e73\u6ed1\u6570\u636e\uff0c\u4ece\u800c\u53bb\u9664\u77ed\u671f\u7684\u6ce2\u52a8\u548c\u957f\u671f\u7684\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u5e94\u7528\u79fb\u52a8\u5e73\u5747\u6cd5\u7684\u6b65\u9aa4<\/strong><\/p>\n<\/p>\n<p><p>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528<code>pandas<\/code>\u5e93\u4e2d\u7684<code>rolling<\/code>\u65b9\u6cd5\u6765\u5b9e\u73b0\u79fb\u52a8\u5e73\u5747\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n<p>data = pd.Series([1, 2, 4, 7, 11, 16, 22])<\/p>\n<h2><strong>\u8ba1\u7b97\u79fb\u52a8\u5e73\u5747<\/strong><\/h2>\n<p>moving_average = data.rolling(window=3).mean()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u79fb\u52a8\u5e73\u5747\u6cd5\u7684\u4f18\u7f3a\u70b9<\/strong><\/p>\n<\/p>\n<p><p><strong>\u4f18\u70b9<\/strong>\uff1a\u79fb\u52a8\u5e73\u5747\u6cd5\u7b80\u5355\u6613\u884c\uff0c\u9002\u7528\u4e8e\u5e73\u6ed1\u548c\u53bb\u9664\u77ed\u671f\u6ce2\u52a8\u3002<\/p>\n<\/p>\n<p><p><strong>\u7f3a\u70b9<\/strong>\uff1a\u79fb\u52a8\u5e73\u5747\u6cd5\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6570\u636e\u7684\u6ede\u540e\u6548\u5e94\uff0c\u5f71\u54cd\u5bf9\u5b9e\u9645\u8d8b\u52bf\u7684\u5224\u65ad\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u56db\u3001\u5176\u4ed6\u53bb\u8d8b\u52bf\u65b9\u6cd5<\/p>\n<\/p>\n<p><p>\u9664\u4e86\u4e0a\u8ff0\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u53bb\u8d8b\u52bf\u7684\u65b9\u6cd5\uff0c\u4f8b\u5982\u591a\u9879\u5f0f\u62df\u5408\u3001\u6307\u6570\u5e73\u6ed1\u7b49\u3002<\/p>\n<\/p>\n<ol>\n<li>\n<p><strong>\u591a\u9879\u5f0f\u62df\u5408<\/strong><\/p>\n<\/p>\n<p><p>\u591a\u9879\u5f0f\u62df\u5408\u901a\u8fc7\u62df\u5408\u4e00\u4e2a\u591a\u9879\u5f0f\u51fd\u6570\u6765\u903c\u8fd1\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u3002\u9002\u7528\u4e8e\u590d\u6742\u7684\u975e\u7ebf\u6027\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import matplotlib.pyplot as plt<\/p>\n<h2><strong>\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n<p>x = np.arange(10)<\/p>\n<p>y = np.array([1, 3, 6, 10, 15, 21, 28, 36, 45, 55])<\/p>\n<h2><strong>\u591a\u9879\u5f0f\u62df\u5408<\/strong><\/h2>\n<p>z = np.polyfit(x, y, 2)<\/p>\n<p>p = np.poly1d(z)<\/p>\n<h2><strong>\u7ed8\u5236\u62df\u5408\u7ed3\u679c<\/strong><\/h2>\n<p>plt.plot(x, y, &#39;o&#39;, label=&#39;Original data&#39;)<\/p>\n<p>plt.plot(x, p(x), &#39;-&#39;, label=&#39;Fitted line&#39;)<\/p>\n<p>plt.legend()<\/p>\n<p>plt.show()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<li>\n<p><strong>\u6307\u6570\u5e73\u6ed1<\/strong><\/p>\n<\/p>\n<p><p>\u6307\u6570\u5e73\u6ed1\u901a\u8fc7\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8d4b\u4e88\u4e0d\u540c\u7684\u6743\u91cd\uff0c\u4ee5\u66f4\u91cd\u89c6\u8fd1\u671f\u7684\u6570\u636e\u70b9\uff0c\u9002\u7528\u4e8e\u53bb\u9664\u5e73\u7a33\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import pandas as pd<\/p>\n<h2><strong>\u5bfc\u5165\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n<p>data = pd.Series([1, 2, 4, 7, 11, 16, 22])<\/p>\n<h2><strong>\u5e94\u7528\u6307\u6570\u5e73\u6ed1<\/strong><\/h2>\n<p>exponential_smoothing = data.ewm(span=3).mean()<\/p>\n<p><\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n<p><p>\u5728\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\uff0c\u53bb\u9664\u8d8b\u52bf\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u7684\u672c\u8d28\u7279\u5f81\u3002\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u7279\u6027\u548c\u5206\u6790\u7684\u76ee\u7684\u3002\u901a\u8fc7\u5b9e\u8df5\u548c\u7ecf\u9a8c\u79ef\u7d2f\uff0c\u53ef\u4ee5\u66f4\u51c6\u786e\u5730\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u5904\u7406\u5b9e\u9645\u95ee\u9898\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p> <strong>\u5982\u4f55\u5728Python\u4e2d\u8bc6\u522b\u6570\u636e\u7684\u8d8b\u52bf\uff1f<\/strong><br \/>\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\u6765\u8bc6\u522b\u6570\u636e\u7684\u8d8b\u52bf\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u7edf\u8ba1\u6a21\u578b\uff08\u5982\u7ebf\u6027\u56de\u5f52\uff09\u548c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002\u5e93\u5982Pandas\u3001NumPy\u548cStatsmodels\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u5206\u6790\u6570\u636e\u96c6\u7684\u8d8b\u52bf\u3002\u901a\u8fc7\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u6216Seaborn\uff09\uff0c\u53ef\u4ee5\u76f4\u89c2\u5c55\u793a\u6570\u636e\u7684\u53d8\u5316\u8d8b\u52bf\uff0c\u5e2e\u52a9\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u80cc\u540e\u7684\u542b\u4e49\u3002<\/p>\n<p><strong>\u5728Python\u4e2d\u53bb\u8d8b\u52bf\u7684\u5e38\u7528\u65b9\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong><br 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