{"id":3543,"date":"2025-06-28T13:00:21","date_gmt":"2025-06-28T05:00:21","guid":{"rendered":"http:\/\/viplao.com\/?p=3543"},"modified":"2025-06-28T13:39:51","modified_gmt":"2025-06-28T05:39:51","slug":"%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/","title":{"rendered":"\u3010Python10\u5e74\u7ecf\u9a8c\u603b\u7ed3\u3011\u7b2c\u4e94\u8bfe \u7535\u5546\u5e73\u53f0\u9500\u552e\u6570\u636e\u5206\u6790\u5b9e\u8df5\u5206\u89e3 \u2013 \u8d8b\u52bf\u9884\u4f30\uff08Trend Forecasting\uff09"},"content":{"rendered":"\n<p>\u5e38\u89c1\u7528\u7684\u9884\u4f30\u573a\u666f\uff1a<\/p>\n\n\n\n<ol>\n<li>\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4e0b\u6708\u9500\u552e\u989d<\/li>\n\n\n\n<li>\u4f7f\u7528ARIMA\u6a21\u578b\u9884\u6d4b\u672a\u6765\u4e00\u5468\u9500\u91cf<\/li>\n\n\n\n<li>\u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8282\u5047\u65e5\u6548\u5e94<\/li>\n\n\n\n<li>\u4f7f\u7528\u6307\u6570\u5e73\u6ed1\u6cd5\u9884\u6d4b\u5355\u54c1\u9500\u91cf<\/li>\n\n\n\n<li>\u6784\u5efa\u5b63\u8282\u6027\u5206\u89e3\u6a21\u578b\uff08STL\uff09<\/li>\n\n\n\n<li>\u4f7f\u7528XGBoost\u9884\u6d4b\u4e0d\u540c\u54c1\u7c7b\u7684\u589e\u957f\u8d8b\u52bf<\/li>\n\n\n\n<li>\u6784\u5efa\u591a\u7ef4\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff08\u6309\u533a\u57df+\u54c1\u7c7b\uff09<\/li>\n\n\n\n<li>\u9884\u6d4b\u4fc3\u9500\u671f\u95f4\u7684\u8ba2\u5355\u6ce2\u52a8<\/li>\n\n\n\n<li>\u9884\u6d4b\u5e93\u5b58\u9700\u6c42\u4ee5\u652f\u6301\u8865\u8d27\u51b3\u7b56<\/li>\n\n\n\n<li>\u4f7f\u7528Prophet\u9884\u6d4b\u5e74\u5ea6\u8d8b\u52bf\u53d8\u5316<\/li>\n<\/ol>\n\n\n\n<p>\u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u8fdb\u884c\u8fd9\u4e9b\u5e38\u89c1\u7684\u8d8b\u52bf\u9884\u4f30\u4efb\u52a1\u3002\u4e3a\u4e86\u6f14\u793a\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528\u4e00\u4e9b\u5e38\u7528\u7684\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u5e93\uff0c\u5982 <code>statsmodels<\/code>\u3001<code>pmdarima<\/code>\u3001<code>xgboost<\/code> \u548c <code>fbprophet<\/code>\uff08\u73b0\u5728\u79f0\u4e3a <code>prophet<\/code>\uff09\u3002\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86\u8fd9\u4e9b\u5e93\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install statsmodels pmdarima xgboost prophet<\/code><\/pre>\n\n\n\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame\u6765\u6a21\u62df\u539f\u59cb\u6570\u636e\uff0c\u5e76\u9010\u6b65\u5e94\u7528\u8fd9\u4e9b\u8d8b\u52bf\u9884\u4f30\u4efb\u52a1\u3002<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u6587\u7ae0\u76ee\u5f55<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 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href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#1_%E4%BD%BF%E7%94%A8%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E9%A2%84%E6%B5%8B%E4%B8%8B%E6%9C%88%E9%94%80%E5%94%AE%E9%A2%9D\" title=\"1. \u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4e0b\u6708\u9500\u552e\u989d\">1. \u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4e0b\u6708\u9500\u552e\u989d<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#2_%E4%BD%BF%E7%94%A8ARIMA%E6%A8%A1%E5%9E%8B%E9%A2%84%E6%B5%8B%E6%9C%AA%E6%9D%A5%E4%B8%80%E5%91%A8%E9%94%80%E9%87%8F\" title=\"2. \u4f7f\u7528ARIMA\u6a21\u578b\u9884\u6d4b\u672a\u6765\u4e00\u5468\u9500\u91cf\">2. \u4f7f\u7528ARIMA\u6a21\u578b\u9884\u6d4b\u672a\u6765\u4e00\u5468\u9500\u91cf<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#3_%E6%9E%84%E5%BB%BA%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B%E8%8A%82%E5%81%87%E6%97%A5%E6%95%88%E5%BA%94\" title=\"3. \u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8282\u5047\u65e5\u6548\u5e94\">3. \u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8282\u5047\u65e5\u6548\u5e94<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#4_%E4%BD%BF%E7%94%A8%E6%8C%87%E6%95%B0%E5%B9%B3%E6%BB%91%E6%B3%95%E9%A2%84%E6%B5%8B%E5%8D%95%E5%93%81%E9%94%80%E9%87%8F\" title=\"4. \u4f7f\u7528\u6307\u6570\u5e73\u6ed1\u6cd5\u9884\u6d4b\u5355\u54c1\u9500\u91cf\">4. \u4f7f\u7528\u6307\u6570\u5e73\u6ed1\u6cd5\u9884\u6d4b\u5355\u54c1\u9500\u91cf<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#5_%E6%9E%84%E5%BB%BA%E5%AD%A3%E8%8A%82%E6%80%A7%E5%88%86%E8%A7%A3%E6%A8%A1%E5%9E%8B%EF%BC%88STL%EF%BC%89\" title=\"5. \u6784\u5efa\u5b63\u8282\u6027\u5206\u89e3\u6a21\u578b\uff08STL\uff09\">5. \u6784\u5efa\u5b63\u8282\u6027\u5206\u89e3\u6a21\u578b\uff08STL\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#6_%E4%BD%BF%E7%94%A8XGBoost%E9%A2%84%E6%B5%8B%E4%B8%8D%E5%90%8C%E5%93%81%E7%B1%BB%E7%9A%84%E5%A2%9E%E9%95%BF%E8%B6%8B%E5%8A%BF\" title=\"6. \u4f7f\u7528XGBoost\u9884\u6d4b\u4e0d\u540c\u54c1\u7c7b\u7684\u589e\u957f\u8d8b\u52bf\">6. \u4f7f\u7528XGBoost\u9884\u6d4b\u4e0d\u540c\u54c1\u7c7b\u7684\u589e\u957f\u8d8b\u52bf<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#7_%E6%9E%84%E5%BB%BA%E5%A4%9A%E7%BB%B4%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B%EF%BC%88%E6%8C%89%E5%8C%BA%E5%9F%9F%E5%93%81%E7%B1%BB%EF%BC%89\" title=\"7. \u6784\u5efa\u591a\u7ef4\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff08\u6309\u533a\u57df+\u54c1\u7c7b\uff09\">7. \u6784\u5efa\u591a\u7ef4\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff08\u6309\u533a\u57df+\u54c1\u7c7b\uff09<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#8_%E9%A2%84%E6%B5%8B%E4%BF%83%E9%94%80%E6%9C%9F%E9%97%B4%E7%9A%84%E8%AE%A2%E5%8D%95%E6%B3%A2%E5%8A%A8\" title=\"8. \u9884\u6d4b\u4fc3\u9500\u671f\u95f4\u7684\u8ba2\u5355\u6ce2\u52a8\">8. \u9884\u6d4b\u4fc3\u9500\u671f\u95f4\u7684\u8ba2\u5355\u6ce2\u52a8<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#9_%E9%A2%84%E6%B5%8B%E5%BA%93%E5%AD%98%E9%9C%80%E6%B1%82%E4%BB%A5%E6%94%AF%E6%8C%81%E8%A1%A5%E8%B4%A7%E5%86%B3%E7%AD%96\" title=\"9. \u9884\u6d4b\u5e93\u5b58\u9700\u6c42\u4ee5\u652f\u6301\u8865\u8d27\u51b3\u7b56\">9. \u9884\u6d4b\u5e93\u5b58\u9700\u6c42\u4ee5\u652f\u6301\u8865\u8d27\u51b3\u7b56<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/#10_%E4%BD%BF%E7%94%A8Prophet%E9%A2%84%E6%B5%8B%E5%B9%B4%E5%BA%A6%E8%B6%8B%E5%8A%BF%E5%8F%98%E5%8C%96\" title=\"10. \u4f7f\u7528Prophet\u9884\u6d4b\u5e74\u5ea6\u8d8b\u52bf\u53d8\u5316\">10. \u4f7f\u7528Prophet\u9884\u6d4b\u5e74\u5ea6\u8d8b\u52bf\u53d8\u5316<\/a><\/li><\/ul><\/nav><\/div>\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E5%88%9B%E5%BB%BA%E7%A4%BA%E4%BE%8B%E6%95%B0%E6%8D%AE\"><\/span>\u521b\u5efa\u793a\u4f8b\u6570\u636e<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\n\n# \u521b\u5efa\u793a\u4f8b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\ndates = pd.date_range(start='2023-01-01', end='2025-06-30', freq='D')\nnp.random.seed(42)\nsales_data = np.cumsum(np.random.normal(loc=100, scale=20, size=len(dates)))\n\ndata = {\n    'order_date': dates,\n    'amount': sales_data\n}\n\ndf = pd.DataFrame(data)\n\n# \u6dfb\u52a0\u5176\u4ed6\u5b57\u6bb5\u4ee5\u652f\u6301\u591a\u7ef4\u9884\u6d4b\ncategories = &#91;'C1', 'C2', 'C3', 'C4']\nregions = &#91;'Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen']\n\ndf&#91;'category_code'] = np.random.choice(categories, len(dates))\ndf&#91;'region'] = np.random.choice(regions, len(dates))\n\nprint(\"\u539f\u59cb\u6570\u636e:\")\nprint(df.head())<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_%E4%BD%BF%E7%94%A8%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E9%A2%84%E6%B5%8B%E4%B8%8B%E6%9C%88%E9%94%80%E5%94%AE%E9%A2%9D\"><\/span>1. \u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4e0b\u6708\u9500\u552e\u989d<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\n\n# \u63d0\u53d6\u5e74\u4efd\u548c\u6708\u4efd\u4f5c\u4e3a\u7279\u5f81\ndf&#91;'year'] = df&#91;'order_date'].dt.year\ndf&#91;'month'] = df&#91;'order_date'].dt.month\n\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\nX = df&#91;&#91;'year', 'month']]\ny = df&#91;'amount']\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n\n# \u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b\nmodel_lr = LinearRegression()\nmodel_lr.fit(X_train, y_train)\n\n# \u9884\u6d4b\u4e0b\u4e2a\u6708\u7684\u9500\u552e\u989d\nnext_month = pd.DataFrame({'year': &#91;2025], 'month': &#91;7]})\npredicted_sales_lr = model_lr.predict(next_month)\n\nprint(\"\\n\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4e0b\u6708\u9500\u552e\u989d:\")\nprint(predicted_sales_lr&#91;0])<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_%E4%BD%BF%E7%94%A8ARIMA%E6%A8%A1%E5%9E%8B%E9%A2%84%E6%B5%8B%E6%9C%AA%E6%9D%A5%E4%B8%80%E5%91%A8%E9%94%80%E9%87%8F\"><\/span>2. \u4f7f\u7528ARIMA\u6a21\u578b\u9884\u6d4b\u672a\u6765\u4e00\u5468\u9500\u91cf<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.arima.model import ARIMA\n\n# \u6309\u5929\u805a\u5408\u9500\u552e\u989d\ndaily_sales = df.groupby('order_date')&#91;'amount'].sum().reset_index()\n\n# \u8bbe\u7f6e\u65f6\u95f4\u4e3a\u7d22\u5f15\ndaily_sales.set_index('order_date', inplace=True)\n\n# \u62df\u5408ARIMA\u6a21\u578b\nmodel_arima = ARIMA(daily_sales, order=(5, 1, 0))  # \u8fd9\u91cc\u4f7f\u7528 (5, 1, 0) \u4f5c\u4e3a\u793a\u4f8b\u53c2\u6570\nmodel_arima_fit = model_arima.fit()\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u5468\u7684\u9500\u552e\u989d\nforecast_arima = model_arima_fit.forecast(steps=7)\n\nprint(\"\\n\u4f7f\u7528ARIMA\u6a21\u578b\u9884\u6d4b\u672a\u6765\u4e00\u5468\u9500\u91cf:\")\nprint(forecast_arima)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_%E6%9E%84%E5%BB%BA%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B%E8%8A%82%E5%81%87%E6%97%A5%E6%95%88%E5%BA%94\"><\/span>3. \u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8282\u5047\u65e5\u6548\u5e94<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\n# \u6dfb\u52a0\u8282\u5047\u65e5\u6807\u5fd7\nholidays = pd.to_datetime(&#91;\n    '2023-01-01', '2023-02-22', '2023-04-05', '2023-05-01', '2023-10-01',\n    '2024-01-01', '2024-02-10', '2024-04-04', '2024-05-01', '2024-10-01',\n    '2025-01-01', '2025-02-19', '2025-04-04', '2025-05-01', '2025-10-01'\n])\n\ndf&#91;'is_holiday'] = df&#91;'order_date'].isin(holidays).astype(int)\n\n# \u62df\u5408SARIMA\u6a21\u578b\nmodel_sarima = SARIMAX(daily_sales, exog=df&#91;'is_holiday'], order=(5, 1, 0), seasonal_order=(1, 1, 1, 12))\nmodel_sarima_fit = model_sarima.fit()\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\nfuture_dates = pd.date_range(start=daily_sales.index&#91;-1] + pd.Timedelta(days=1), periods=30)\nfuture_exog = df.loc&#91;future_dates]&#91;'is_holiday'].values\nforecast_sarima = model_sarima_fit.get_forecast(steps=30, exog=future_exog)\n\nprint(\"\\n\u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8282\u5047\u65e5\u6548\u5e94:\")\nprint(forecast_sarima.predicted_mean)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_%E4%BD%BF%E7%94%A8%E6%8C%87%E6%95%B0%E5%B9%B3%E6%BB%91%E6%B3%95%E9%A2%84%E6%B5%8B%E5%8D%95%E5%93%81%E9%94%80%E9%87%8F\"><\/span>4. \u4f7f\u7528\u6307\u6570\u5e73\u6ed1\u6cd5\u9884\u6d4b\u5355\u54c1\u9500\u91cf<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing\n\n# \u6309\u4ea7\u54c1ID\u805a\u5408\u9500\u552e\u989d\nproduct_sales = df.groupby(&#91;'order_date', 'product_id'])&#91;'amount'].sum().unstack(fill_value=0)\n\n# \u9009\u62e9\u4e00\u4e2a\u4ea7\u54c1ID\u8fdb\u884c\u9884\u6d4b\nselected_product = product_sales.columns&#91;0]\nproduct_series = product_sales&#91;selected_product]\n\n# \u62df\u5408\u6307\u6570\u5e73\u6ed1\u6a21\u578b\nmodel_es = ExponentialSmoothing(product_series, trend='add', seasonal=None)\nmodel_es_fit = model_es.fit()\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\nforecast_es = model_es_fit.forecast(steps=30)\n\nprint(\"\\n\u4f7f\u7528\u6307\u6570\u5e73\u6ed1\u6cd5\u9884\u6d4b\u5355\u54c1\u9500\u91cf:\")\nprint(forecast_es)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_%E6%9E%84%E5%BB%BA%E5%AD%A3%E8%8A%82%E6%80%A7%E5%88%86%E8%A7%A3%E6%A8%A1%E5%9E%8B%EF%BC%88STL%EF%BC%89\"><\/span>5. \u6784\u5efa\u5b63\u8282\u6027\u5206\u89e3\u6a21\u578b\uff08STL\uff09<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.seasonal import STL\n\n# \u6309\u5929\u805a\u5408\u9500\u552e\u989d\ndaily_sales = df.groupby('order_date')&#91;'amount'].sum().reset_index()\n\n# \u8bbe\u7f6e\u65f6\u95f4\u4e3a\u7d22\u5f15\ndaily_sales.set_index('order_date', inplace=True)\n\n# \u5b63\u8282\u6027\u5206\u89e3\nstl = STL(daily_sales, period=365)\nres = stl.fit()\n\nprint(\"\\n\u6784\u5efa\u5b63\u8282\u6027\u5206\u89e3\u6a21\u578b\uff08STL\uff09:\")\nprint(res.summary())<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_%E4%BD%BF%E7%94%A8XGBoost%E9%A2%84%E6%B5%8B%E4%B8%8D%E5%90%8C%E5%93%81%E7%B1%BB%E7%9A%84%E5%A2%9E%E9%95%BF%E8%B6%8B%E5%8A%BF\"><\/span>6. \u4f7f\u7528XGBoost\u9884\u6d4b\u4e0d\u540c\u54c1\u7c7b\u7684\u589e\u957f\u8d8b\u52bf<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nimport xgboost as xgb\nfrom sklearn.metrics import mean_squared_error\n\n# \u6309\u54c1\u7c7b\u805a\u5408\u9500\u552e\u989d\ncategory_sales = df.groupby(&#91;'order_date', 'category_code'])&#91;'amount'].sum().unstack(fill_value=0)\n\n# \u9009\u62e9\u4e00\u4e2a\u54c1\u7c7b\u8fdb\u884c\u9884\u6d4b\nselected_category = category_sales.columns&#91;0]\ncategory_series = category_sales&#91;selected_category].reset_index()\n\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\ncategory_series&#91;'lag_1'] = category_series&#91;'amount'].shift(1)\ncategory_series&#91;'lag_7'] = category_series&#91;'amount'].shift(7)\ncategory_series.dropna(inplace=True)\n\nX = category_series&#91;&#91;'lag_1', 'lag_7']]\ny = category_series&#91;'amount']\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n\n# \u8bad\u7ec3XGBoost\u6a21\u578b\ndtrain = xgb.DMatrix(X_train, label=y_train)\ndtest = xgb.DMatrix(X_test, label=y_test)\n\nparams = {'objective': 'reg:squarederror'}\nbst = xgb.train(params, dtrain, num_boost_round=100)\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\nfuture_lag_1 = category_series&#91;'amount'].iloc&#91;-1]\nfuture_lag_7 = category_series&#91;'amount'].iloc&#91;-7:]\nfuture_lag_7_avg = future_lag_7.mean()\n\nfuture_X = pd.DataFrame({'lag_1': &#91;future_lag_1], 'lag_7': &#91;future_lag_7_avg]})\nfuture_dmatrix = xgb.DMatrix(future_X)\npredicted_sales_xgb = bst.predict(future_dmatrix)\n\nprint(\"\\n\u4f7f\u7528XGBoost\u9884\u6d4b\u4e0d\u540c\u54c1\u7c7b\u7684\u589e\u957f\u8d8b\u52bf:\")\nprint(predicted_sales_xgb&#91;0])<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_%E6%9E%84%E5%BB%BA%E5%A4%9A%E7%BB%B4%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B%EF%BC%88%E6%8C%89%E5%8C%BA%E5%9F%9F%E5%93%81%E7%B1%BB%EF%BC%89\"><\/span>7. \u6784\u5efa\u591a\u7ef4\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff08\u6309\u533a\u57df+\u54c1\u7c7b\uff09<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nimport xgboost as xgb\nfrom sklearn.metrics import mean_squared_error\n\n# \u6309\u533a\u57df\u548c\u54c1\u7c7b\u805a\u5408\u9500\u552e\u989d\nregion_category_sales = df.groupby(&#91;'order_date', 'region', 'category_code'])&#91;'amount'].sum().unstack(fill_value=0)\n\n# \u9009\u62e9\u4e00\u4e2a\u7ec4\u5408\u8fdb\u884c\u9884\u6d4b\nselected_region = region_category_sales.columns.levels&#91;0]&#91;0]\nselected_category = region_category_sales.columns.levels&#91;1]&#91;0]\nregion_category_series = region_category_sales&#91;(selected_region, selected_category)].reset_index()\n\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\nregion_category_series&#91;'lag_1'] = region_category_series&#91;'amount'].shift(1)\nregion_category_series&#91;'lag_7'] = region_category_series&#91;'amount'].shift(7)\nregion_category_series.dropna(inplace=True)\n\nX = region_category_series&#91;&#91;'lag_1', 'lag_7']]\ny = region_category_series&#91;'amount']\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n\n# \u8bad\u7ec3XGBoost\u6a21\u578b\ndtrain = xgb.DMatrix(X_train, label=y_train)\ndtest = xgb.DMatrix(X_test, label=y_test)\n\nparams = {'objective': 'reg:squarederror'}\nbst = xgb.train(params, dtrain, num_boost_round=100)\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\nfuture_lag_1 = region_category_series&#91;'amount'].iloc&#91;-1]\nfuture_lag_7 = region_category_series&#91;'amount'].iloc&#91;-7:]\nfuture_lag_7_avg = future_lag_7.mean()\n\nfuture_X = pd.DataFrame({'lag_1': &#91;future_lag_1], 'lag_7': &#91;future_lag_7_avg]})\nfuture_dmatrix = xgb.DMatrix(future_X)\npredicted_sales_mc = bst.predict(future_dmatrix)\n\nprint(\"\\n\u6784\u5efa\u591a\u7ef4\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff08\u6309\u533a\u57df+\u54c1\u7c7b\uff09:\")\nprint(predicted_sales_mc&#91;0])<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"8_%E9%A2%84%E6%B5%8B%E4%BF%83%E9%94%80%E6%9C%9F%E9%97%B4%E7%9A%84%E8%AE%A2%E5%8D%95%E6%B3%A2%E5%8A%A8\"><\/span>8. \u9884\u6d4b\u4fc3\u9500\u671f\u95f4\u7684\u8ba2\u5355\u6ce2\u52a8<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nimport xgboost as xgb\nfrom sklearn.metrics import mean_squared_error\n\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\ndf&#91;'lag_1'] = df&#91;'amount'].shift(1)\ndf&#91;'lag_7'] = df&#91;'amount'].shift(7)\ndf.dropna(inplace=True)\n\nX = df&#91;&#91;'lag_1', 'lag_7', 'promotion']]\ny = df&#91;'amount']\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n\n# \u8bad\u7ec3XGBoost\u6a21\u578b\ndtrain = xgb.DMatrix(X_train, label=y_train)\ndtest = xgb.DMatrix(X_test, label=y_test)\n\nparams = {'objective': 'reg:squarederror'}\nbst = xgb.train(params, dtrain, num_boost_round=100)\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\nfuture_lag_1 = df&#91;'amount'].iloc&#91;-1]\nfuture_lag_7 = df&#91;'amount'].iloc&#91;-7:]\nfuture_lag_7_avg = future_lag_7.mean()\nfuture_promotion = 1  # \u5047\u8bbe\u672a\u6765\u662f\u4fc3\u9500\u671f\n\nfuture_X = pd.DataFrame({\n    'lag_1': &#91;future_lag_1],\n    'lag_7': &#91;future_lag_7_avg],\n    'promotion': &#91;future_promotion]\n})\nfuture_dmatrix = xgb.DMatrix(future_X)\npredicted_sales_promo = bst.predict(future_dmatrix)\n\nprint(\"\\n\u9884\u6d4b\u4fc3\u9500\u671f\u95f4\u7684\u8ba2\u5355\u6ce2\u52a8:\")\nprint(predicted_sales_promo&#91;0])<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"9_%E9%A2%84%E6%B5%8B%E5%BA%93%E5%AD%98%E9%9C%80%E6%B1%82%E4%BB%A5%E6%94%AF%E6%8C%81%E8%A1%A5%E8%B4%A7%E5%86%B3%E7%AD%96\"><\/span>9. \u9884\u6d4b\u5e93\u5b58\u9700\u6c42\u4ee5\u652f\u6301\u8865\u8d27\u51b3\u7b56<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing\n\n# \u6309\u4ea7\u54c1ID\u805a\u5408\u9500\u552e\u989d\nproduct_sales = df.groupby(&#91;'order_date', 'product_id'])&#91;'amount'].sum().unstack(fill_value=0)\n\n# \u9009\u62e9\u4e00\u4e2a\u4ea7\u54c1ID\u8fdb\u884c\u9884\u6d4b\nselected_product = product_sales.columns&#91;0]\nproduct_series = product_sales&#91;selected_product]\n\n# \u62df\u5408\u6307\u6570\u5e73\u6ed1\u6a21\u578b\nmodel_es = ExponentialSmoothing(product_series, trend='add', seasonal=None)\nmodel_es_fit = model_es.fit()\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\nforecast_es = model_es_fit.forecast(steps=30)\n\n# \u5047\u8bbe\u5e93\u5b58\u5468\u8f6c\u7387\u4e3a30\u5929\ninventory_turnover_days = 30\naverage_daily_sales = forecast_es.mean()\npredicted_inventory_demand = average_daily_sales * inventory_turnover_days\n\nprint(\"\\n\u9884\u6d4b\u5e93\u5b58\u9700\u6c42\u4ee5\u652f\u6301\u8865\u8d27\u51b3\u7b56:\")\nprint(predicted_inventory_demand)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"10_%E4%BD%BF%E7%94%A8Prophet%E9%A2%84%E6%B5%8B%E5%B9%B4%E5%BA%A6%E8%B6%8B%E5%8A%BF%E5%8F%98%E5%8C%96\"><\/span>10. \u4f7f\u7528Prophet\u9884\u6d4b\u5e74\u5ea6\u8d8b\u52bf\u53d8\u5316<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\nimport numpy as np\nfrom fbprophet import Prophet\n\n# \u6309\u5929\u805a\u5408\u9500\u552e\u989d\ndaily_sales = df.groupby('order_date')&#91;'amount'].sum().reset_index()\n\n# \u91cd\u547d\u540d\u5217\u4ee5\u7b26\u5408Prophet\u7684\u8981\u6c42\ndaily_sales.rename(columns={'order_date': 'ds', 'amount': 'y'}, inplace=True)\n\n# \u521d\u59cb\u5316\u5e76\u62df\u5408Prophet\u6a21\u578b\nmodel_prophet = Prophet()\nmodel_prophet.fit(daily_sales)\n\n# \u521b\u5efa\u672a\u6765\u4e00\u5e74\u7684\u65e5\u671f\u8303\u56f4\nfuture_dates = model_prophet.make_future_dataframe(periods=365)\n\n# \u9884\u6d4b\u672a\u6765\u4e00\u5e74\u7684\u9500\u552e\u989d\nforecast_prophet = model_prophet.predict(future_dates)\n\nprint(\"\\n\u4f7f\u7528Prophet\u9884\u6d4b\u5e74\u5ea6\u8d8b\u52bf\u53d8\u5316:\")\nprint(forecast_prophet&#91;&#91;'ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())<\/code><\/pre>\n\n\n\n<p>\u7efc\u5408\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6700\u7ec8\u7684\u8d8b\u52bf\u9884\u4f30\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n<p>\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86\u4ece\u539f\u59cb\u6570\u636e\u5230\u7ecf\u8fc7\u5168\u9762\u8d8b\u52bf\u9884\u4f30\u7684\u6570\u636e\u7684\u8fc7\u7a0b\u3002\u4f60\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8c03\u6574\u6bcf\u4e00\u6b65\u7684\u64cd\u4f5c\u3002<\/p>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.model_selection import train_test_split\r\nfrom statsmodels.tsa.arima.model import ARIMA\r\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\r\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing\r\nfrom statsmodels.tsa.seasonal import STL\r\nimport xgboost as xgb\r\nfrom fbprophet import Prophet\r\n\r\n# \u521b\u5efa\u793a\u4f8b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\r\ndates = pd.date_range(start='2023-01-01', end='2025-06-30', freq='D')\r\nnp.random.seed(42)\r\nsales_data = np.cumsum(np.random.normal(loc=100, scale=20, size=len(dates)))\r\n\r\ndata = {\r\n    'order_date': dates,\r\n    'amount': sales_data\r\n}\r\n\r\ndf = pd.DataFrame(data)\r\n\r\n# \u6dfb\u52a0\u5176\u4ed6\u5b57\u6bb5\u4ee5\u652f\u6301\u591a\u7ef4\u9884\u6d4b\r\ncategories = &#91;'C1', 'C2', 'C3', 'C4']\r\nregions = &#91;'Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen']\r\n\r\ndf&#91;'category_code'] = np.random.choice(categories, len(dates))\r\ndf&#91;'region'] = np.random.choice(regions, len(dates))\r\n\r\n# \u63d0\u53d6\u5e74\u4efd\u548c\u6708\u4efd\u4f5c\u4e3a\u7279\u5f81\r\ndf&#91;'year'] = df&#91;'order_date'].dt.year\r\ndf&#91;'month'] = df&#91;'order_date'].dt.month\r\n\r\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\r\nX = df&#91;&#91;'year', 'month']]\r\ny = df&#91;'amount']\r\n\r\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\r\n\r\n# \u8bad\u7ec3\u7ebf\u6027\u56de\u5f52\u6a21\u578b\r\nmodel_lr = LinearRegression()\r\nmodel_lr.fit(X_train, y_train)\r\n\r\n# \u9884\u6d4b\u4e0b\u4e2a\u6708\u7684\u9500\u552e\u989d\r\nnext_month = pd.DataFrame({'year': &#91;2025], 'month': &#91;7]})\r\npredicted_sales_lr = model_lr.predict(next_month)\r\n\r\nprint(\"\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u9884\u6d4b\u4e0b\u6708\u9500\u552e\u989d:\")\r\nprint(predicted_sales_lr&#91;0])\r\n\r\n# \u6309\u5929\u805a\u5408\u9500\u552e\u989d\r\ndaily_sales = df.groupby('order_date')&#91;'amount'].sum().reset_index()\r\n\r\n# \u8bbe\u7f6e\u65f6\u95f4\u4e3a\u7d22\u5f15\r\ndaily_sales.set_index('order_date', inplace=True)\r\n\r\n# \u62df\u5408ARIMA\u6a21\u578b\r\nmodel_arima = ARIMA(daily_sales, order=(5, 1, 0))  # \u8fd9\u91cc\u4f7f\u7528 (5, 1, 0) \u4f5c\u4e3a\u793a\u4f8b\u53c2\u6570\r\nmodel_arima_fit = model_arima.fit()\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u5468\u7684\u9500\u552e\u989d\r\nforecast_arima = model_arima_fit.forecast(steps=7)\r\n\r\nprint(\"\\n\u4f7f\u7528ARIMA\u6a21\u578b\u9884\u6d4b\u672a\u6765\u4e00\u5468\u9500\u91cf:\")\r\nprint(forecast_arima)\r\n\r\n# \u6dfb\u52a0\u8282\u5047\u65e5\u6807\u5fd7\r\nholidays = pd.to_datetime(&#91;\r\n    '2023-01-01', '2023-02-22', '2023-04-05', '2023-05-01', '2023-10-01',\r\n    '2024-01-01', '2024-02-10', '2024-04-04', '2024-05-01', '2024-10-01',\r\n    '2025-01-01', '2025-02-19', '2025-04-04', '2025-05-01', '2025-10-01'\r\n])\r\n\r\ndf&#91;'is_holiday'] = df&#91;'order_date'].isin(holidays).astype(int)\r\n\r\n# \u62df\u5408SARIMA\u6a21\u578b\r\nmodel_sarima = SARIMAX(daily_sales, exog=df&#91;'is_holiday'], order=(5, 1, 0), seasonal_order=(1, 1, 1, 12))\r\nmodel_sarima_fit = model_sarima.fit()\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\r\nfuture_dates = pd.date_range(start=daily_sales.index&#91;-1] + pd.Timedelta(days=1), periods=30)\r\nfuture_exog = df.loc&#91;future_dates]&#91;'is_holiday'].values\r\nforecast_sarima = model_sarima_fit.get_forecast(steps=30, exog=future_exog)\r\n\r\nprint(\"\\n\u6784\u5efa\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u8282\u5047\u65e5\u6548\u5e94:\")\r\nprint(forecast_sarima.predicted_mean)\r\n\r\n# \u6309\u4ea7\u54c1ID\u805a\u5408\u9500\u552e\u989d\r\nproduct_sales = df.groupby(&#91;'order_date', 'product_id'])&#91;'amount'].sum().unstack(fill_value=0)\r\n\r\n# \u9009\u62e9\u4e00\u4e2a\u4ea7\u54c1ID\u8fdb\u884c\u9884\u6d4b\r\nselected_product = product_sales.columns&#91;0]\r\nproduct_series = product_sales&#91;selected_product]\r\n\r\n# \u62df\u5408\u6307\u6570\u5e73\u6ed1\u6a21\u578b\r\nmodel_es = ExponentialSmoothing(product_series, trend='add', seasonal=None)\r\nmodel_es_fit = model_es.fit()\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\r\nforecast_es = model_es_fit.forecast(steps=30)\r\n\r\nprint(\"\\n\u4f7f\u7528\u6307\u6570\u5e73\u6ed1\u6cd5\u9884\u6d4b\u5355\u54c1\u9500\u91cf:\")\r\nprint(forecast_es)\r\n\r\n# \u5b63\u8282\u6027\u5206\u89e3\r\nstl = STL(daily_sales, period=365)\r\nres = stl.fit()\r\n\r\nprint(\"\\n\u6784\u5efa\u5b63\u8282\u6027\u5206\u89e3\u6a21\u578b\uff08STL\uff09:\")\r\nprint(res.summary())\r\n\r\n# \u6309\u54c1\u7c7b\u805a\u5408\u9500\u552e\u989d\r\ncategory_sales = df.groupby(&#91;'order_date', 'category_code'])&#91;'amount'].sum().unstack(fill_value=0)\r\n\r\n# \u9009\u62e9\u4e00\u4e2a\u54c1\u7c7b\u8fdb\u884c\u9884\u6d4b\r\nselected_category = category_sales.columns&#91;0]\r\ncategory_series = category_sales&#91;selected_category].reset_index()\r\n\r\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\r\ncategory_series&#91;'lag_1'] = category_series&#91;'amount'].shift(1)\r\ncategory_series&#91;'lag_7'] = category_series&#91;'amount'].shift(7)\r\ncategory_series.dropna(inplace=True)\r\n\r\nX = category_series&#91;&#91;'lag_1', 'lag_7']]\r\ny = category_series&#91;'amount']\r\n\r\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\r\n\r\n# \u8bad\u7ec3XGBoost\u6a21\u578b\r\ndtrain = xgb.DMatrix(X_train, label=y_train)\r\ndtest = xgb.DMatrix(X_test, label=y_test)\r\n\r\nparams = {'objective': 'reg:squarederror'}\r\nbst = xgb.train(params, dtrain, num_boost_round=100)\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\r\nfuture_lag_1 = category_series&#91;'amount'].iloc&#91;-1]\r\nfuture_lag_7 = category_series&#91;'amount'].iloc&#91;-7:]\r\nfuture_lag_7_avg = future_lag_7.mean()\r\n\r\nfuture_X = pd.DataFrame({'lag_1': &#91;future_lag_1], 'lag_7': &#91;future_lag_7_avg]})\r\nfuture_dmatrix = xgb.DMatrix(future_X)\r\npredicted_sales_xgb = bst.predict(future_dmatrix)\r\n\r\nprint(\"\\n\u4f7f\u7528XGBoost\u9884\u6d4b\u4e0d\u540c\u54c1\u7c7b\u7684\u589e\u957f\u8d8b\u52bf:\")\r\nprint(predicted_sales_xgb&#91;0])\r\n\r\n# \u6309\u533a\u57df\u548c\u54c1\u7c7b\u805a\u5408\u9500\u552e\u989d\r\nregion_category_sales = df.groupby(&#91;'order_date', 'region', 'category_code'])&#91;'amount'].sum().unstack(fill_value=0)\r\n\r\n# \u9009\u62e9\u4e00\u4e2a\u7ec4\u5408\u8fdb\u884c\u9884\u6d4b\r\nselected_region = region_category_sales.columns.levels&#91;0]&#91;0]\r\nselected_category = region_category_sales.columns.levels&#91;1]&#91;0]\r\nregion_category_series = region_category_sales&#91;(selected_region, selected_category)].reset_index()\r\n\r\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\r\nregion_category_series&#91;'lag_1'] = region_category_series&#91;'amount'].shift(1)\r\nregion_category_series&#91;'lag_7'] = region_category_series&#91;'amount'].shift(7)\r\nregion_category_series.dropna(inplace=True)\r\n\r\nX = region_category_series&#91;&#91;'lag_1', 'lag_7']]\r\ny = region_category_series&#91;'amount']\r\n\r\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\r\n\r\n# \u8bad\u7ec3XGBoost\u6a21\u578b\r\ndtrain = xgb.DMatrix(X_train, label=y_train)\r\ndtest = xgb.DMatrix(X_test, label=y_test)\r\n\r\nparams = {'objective': 'reg:squarederror'}\r\nbst = xgb.train(params, dtrain, num_boost_round=100)\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\r\nfuture_lag_1 = region_category_series&#91;'amount'].iloc&#91;-1]\r\nfuture_lag_7 = region_category_series&#91;'amount'].iloc&#91;-7:]\r\nfuture_lag_7_avg = future_lag_7.mean()\r\n\r\nfuture_X = pd.DataFrame({'lag_1': &#91;future_lag_1], 'lag_7': &#91;future_lag_7_avg]})\r\nfuture_dmatrix = xgb.DMatrix(future_X)\r\npredicted_sales_mc = bst.predict(future_dmatrix)\r\n\r\nprint(\"\\n\u6784\u5efa\u591a\u7ef4\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff08\u6309\u533a\u57df+\u54c1\u7c7b\uff09:\")\r\nprint(predicted_sales_mc&#91;0])\r\n\r\n# \u51c6\u5907\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\r\ndf&#91;'lag_1'] = df&#91;'amount'].shift(1)\r\ndf&#91;'lag_7'] = df&#91;'amount'].shift(7)\r\ndf.dropna(inplace=True)\r\n\r\nX = df&#91;&#91;'lag_1', 'lag_7', 'promotion']]\r\ny = df&#91;'amount']\r\n\r\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\r\n\r\n# \u8bad\u7ec3XGBoost\u6a21\u578b\r\ndtrain = xgb.DMatrix(X_train, label=y_train)\r\ndtest = xgb.DMatrix(X_test, label=y_test)\r\n\r\nparams = {'objective': 'reg:squarederror'}\r\nbst = xgb.train(params, dtrain, num_boost_round=100)\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u4e2a\u6708\u7684\u9500\u552e\u989d\r\nfuture_lag_1 = df&#91;'amount'].iloc&#91;-1]\r\nfuture_lag_7 = df&#91;'amount'].iloc&#91;-7:]\r\nfuture_lag_7_avg = future_lag_7.mean()\r\nfuture_promotion = 1  # \u5047\u8bbe\u672a\u6765\u662f\u4fc3\u9500\u671f\r\n\r\nfuture_X = pd.DataFrame({\r\n    'lag_1': &#91;future_lag_1],\r\n    'lag_7': &#91;future_lag_7_avg],\r\n    'promotion': &#91;future_promotion]\r\n})\r\nfuture_dmatrix = xgb.DMatrix(future_X)\r\npredicted_sales_promo = bst.predict(future_dmatrix)\r\n\r\nprint(\"\\n\u9884\u6d4b\u4fc3\u9500\u671f\u95f4\u7684\u8ba2\u5355\u6ce2\u52a8:\")\r\nprint(predicted_sales_promo&#91;0])\r\n\r\n# \u5047\u8bbe\u5e93\u5b58\u5468\u8f6c\u7387\u4e3a30\u5929\r\ninventory_turnover_days = 30\r\naverage_daily_sales = forecast_es.mean()\r\npredicted_inventory_demand = average_daily_sales * inventory_turnover_days\r\n\r\nprint(\"\\n\u9884\u6d4b\u5e93\u5b58\u9700\u6c42\u4ee5\u652f\u6301\u8865\u8d27\u51b3\u7b56:\")\r\nprint(predicted_inventory_demand)\r\n\r\n# \u91cd\u547d\u540d\u5217\u4ee5\u7b26\u5408Prophet\u7684\u8981\u6c42\r\ndaily_sales.rename(columns={'order_date': 'ds', 'amount': 'y'}, inplace=True)\r\n\r\n# \u521d\u59cb\u5316\u5e76\u62df\u5408Prophet\u6a21\u578b\r\nmodel_prophet = Prophet()\r\nmodel_prophet.fit(daily_sales)\r\n\r\n# \u521b\u5efa\u672a\u6765\u4e00\u5e74\u7684\u65e5\u671f\u8303\u56f4\r\nfuture_dates = model_prophet.make_future_dataframe(periods=365)\r\n\r\n# \u9884\u6d4b\u672a\u6765\u4e00\u5e74\u7684\u9500\u552e\u989d\r\nforecast_prophet = model_prophet.predict(future_dates)\r\n\r\nprint(\"\\n\u4f7f\u7528Prophet\u9884\u6d4b\u5e74\u5ea6\u8d8b\u52bf\u53d8\u5316:\")\r\nprint(forecast_prophet&#91;&#91;'ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())\r\n\r\n\r\n\r\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>\u5e38\u89c1\u7528\u7684\u9884\u4f30\u573a\u666f\uff1a \u6211\u4eec\u5c06\u5c55\u793a\u5982\u4f55\u8fdb\u884c\u8fd9\u4e9b\u5e38\u89c1\u7684\u8d8b\u52bf\u9884\u4f30\u4efb\u52a1\u3002\u4e3a\u4e86\u6f14\u793a\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u9700\u8981\u4f7f\u7528\u4e00\u4e9b\u5e38\u7528&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/06\/28\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e7%bb%8f%e9%aa%8c%e3%80%91%e7%94%b5%e5%95%86%e5%b9%b3%e5%8f%b0%e9%94%80%e5%94%ae%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e5%ae%9e%e8%b7%b5%e5%88%86%e8%a7%a3-3\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010Python10\u5e74\u7ecf\u9a8c\u603b\u7ed3\u3011\u7b2c\u4e94\u8bfe \u7535\u5546\u5e73\u53f0\u9500\u552e\u6570\u636e\u5206\u6790\u5b9e\u8df5\u5206\u89e3 \u2013 \u8d8b\u52bf\u9884\u4f30\uff08Trend Forecasting\uff09<\/span><i class=\"fa fa-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":428,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3543"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=3543"}],"version-history":[{"count":2,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3543\/revisions"}],"predecessor-version":[{"id":3563,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/3543\/revisions\/3563"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=3543"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=3543"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=3543"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}