{"id":1095257,"date":"2025-01-08T14:49:25","date_gmt":"2025-01-08T06:49:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1095257.html"},"modified":"2025-01-08T14:49:27","modified_gmt":"2025-01-08T06:49:27","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%b7%ae%e5%88%86%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1095257.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u5dee\u5206\u6570\u636e\u5206\u6790"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u5dee\u5206\u6570\u636e\u5206\u6790\"<\/p>\n

\u5728Python\u4e2d\u8fdb\u884c\u5dee\u5206\u6570\u636e\u5206\u6790\uff0c\u6838\u5fc3\u89c2\u70b9\u5305\u62ec\uff1a\u5dee\u5206\u7684\u57fa\u672c\u6982\u5ff5\u3001\u5dee\u5206\u5728\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u7684\u5e94\u7528\u3001\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\u3001\u5dee\u5206\u7684\u9006\u64cd\u4f5c<\/strong>\u3002\u5dee\u5206\u7684\u57fa\u672c\u6982\u5ff5\u662f\u6307\u8ba1\u7b97\u5e8f\u5217\u4e2d\u76f8\u90bb\u6570\u636e\u70b9\u7684\u5dee\u503c\uff0c\u4ee5\u6b64\u6765\u6d88\u9664\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u4f7f\u5e8f\u5217\u66f4\u52a0\u7a33\u5b9a\u3002\u63a5\u4e0b\u6765\u6211\u4eec\u8be6\u7ec6\u8bb2\u89e3\u5982\u4f55\u5229\u7528Pandas\u5e93\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u5dee\u5206\u64cd\u4f5c\u53ef\u4ee5\u901a\u8fc7Pandas\u5e93\u4e2d\u7684diff()<\/code>\u51fd\u6570\u8f7b\u677e\u5b9e\u73b0\u3002diff()<\/code>\u51fd\u6570\u7528\u4e8e\u8ba1\u7b97DataFrame\u6216Series\u5bf9\u8c61\u4e2d\u76f8\u90bb\u5143\u7d20\u7684\u5dee\u503c\u3002\u901a\u8fc7\u6307\u5b9aperiods<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u63a7\u5236\u8ba1\u7b97\u5dee\u503c\u7684\u95f4\u9694\u3002\u4f8b\u5982\uff0cdata.diff(periods=1)<\/code>\u8ba1\u7b97\u7684\u662f\u4e00\u9636\u5dee\u5206\uff0c\u5373\u5f53\u524d\u503c\u51cf\u53bb\u524d\u4e00\u4e2a\u503c\u7684\u5dee\uff1bdata.diff(periods=2)<\/code>\u8ba1\u7b97\u7684\u662f\u4e8c\u9636\u5dee\u5206\uff0c\u5373\u5f53\u524d\u503c\u51cf\u53bb\u524d\u4e24\u4e2a\u503c\u7684\u5dee\u3002\u5dee\u5206\u64cd\u4f5c\u5e38\u7528\u4e8e\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\uff0c\u4ee5\u6d88\u9664\u6570\u636e\u4e2d\u7684\u8d8b\u52bf\u6210\u5206\uff0c\u8fbe\u5230\u7a33\u5b9a\u5e8f\u5217\u7684\u76ee\u7684\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u5728Python\u4e2d\u8fdb\u884c\u5dee\u5206\u6570\u636e\u5206\u6790\u7684\u5404\u4e2a\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5dee\u5206\u7684\u57fa\u672c\u6982\u5ff5<\/h3>\n<\/p>\n

\u5dee\u5206\u662f\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u7684\u4e00\u79cd\u5e38\u7528\u6280\u672f\u3002\u5b83\u901a\u8fc7\u8ba1\u7b97\u5e8f\u5217\u4e2d\u76f8\u90bb\u6570\u636e\u70b9\u7684\u5dee\u503c\u6765\u6d88\u9664\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u4f7f\u5e8f\u5217\u66f4\u52a0\u7a33\u5b9a\u3002\u7a33\u5b9a\u7684\u5e8f\u5217\u6709\u52a9\u4e8e\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u4e00\u9636\u5dee\u5206\uff1a<\/strong> \u4e00\u9636\u5dee\u5206\u662f\u6700\u57fa\u672c\u7684\u5dee\u5206\u5f62\u5f0f\uff0c\u8868\u793a\u5f53\u524d\u503c\u4e0e\u524d\u4e00\u4e2a\u503c\u4e4b\u5dee\uff0c\u516c\u5f0f\u4e3a\uff1ay_t' = y_t - y_(t-1)<\/code>\u3002\u4e00\u9636\u5dee\u5206\u901a\u5e38\u7528\u4e8e\u6d88\u9664\u7ebf\u6027\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u4e8c\u9636\u5dee\u5206\uff1a<\/strong> \u4e8c\u9636\u5dee\u5206\u8868\u793a\u5f53\u524d\u503c\u4e0e\u524d\u4e24\u4e2a\u503c\u4e4b\u5dee\uff0c\u516c\u5f0f\u4e3a\uff1ay_t'' = y_t - 2*y_(t-1) + y_(t-2)<\/code>\u3002\u4e8c\u9636\u5dee\u5206\u901a\u5e38\u7528\u4e8e\u6d88\u9664\u975e\u7ebf\u6027\u8d8b\u52bf\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u5dee\u5206\u5728\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n

    \u5dee\u5206\u5e7f\u6cdb\u5e94\u7528\u4e8e\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\uff0c\u5c24\u5176\u662f\u5728ARIMA\u6a21\u578b\u7684\u6784\u5efa\u8fc7\u7a0b\u4e2d\u3002ARIMA\u6a21\u578b\u4e2d\u7684I<\/code>\u8868\u793a\u5dee\u5206\u64cd\u4f5c\uff0c\u7528\u4e8e\u5c06\u975e\u5e73\u7a33\u5e8f\u5217\u8f6c\u6362\u4e3a\u5e73\u7a33\u5e8f\u5217\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      \u7a33\u5b9a\u6027\u68c0\u9a8c\uff1a<\/strong> \u901a\u8fc7\u5dee\u5206\u64cd\u4f5c\uff0c\u53ef\u4ee5\u6d88\u9664\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u4f7f\u5e8f\u5217\u53d8\u5f97\u7a33\u5b9a\u3002\u7a33\u5b9a\u7684\u5e8f\u5217\u5177\u6709\u6052\u5b9a\u7684\u5747\u503c\u548c\u65b9\u5dee\uff0c\u6709\u52a9\u4e8e\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u7cbe\u5ea6\u3002<\/p>\n<\/p>\n<\/li>\n

    2. \n

      \u6a21\u578b\u6784\u5efa\uff1a<\/strong> \u5728\u6784\u5efaARIMA\u6a21\u578b\u65f6\uff0c\u9700\u8981\u5bf9\u5e8f\u5217\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\uff0c\u4ee5\u786e\u4fdd\u5e8f\u5217\u7684\u5e73\u7a33\u6027\u3002\u5dee\u5206\u64cd\u4f5c\u540e\u7684\u5e8f\u5217\u53ef\u4ee5\u7528\u4e8e\u6a21\u578b\u53c2\u6570\u7684\u4f30\u8ba1\u548c\u9884\u6d4b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

      \u4e09\u3001\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c<\/h3>\n<\/p>\n

      Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\u7684\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

      1. \u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n

      \u9996\u5148\uff0c\u9700\u8981\u5bfc\u5165Pandas\u5e93\u3002Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002<\/p>\n<\/p>\n

      import pandas as pd<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2. \u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
      \u63a5\u4e0b\u6765\uff0c\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u7528\u4e8e\u6f14\u793a\u5dee\u5206\u64cd\u4f5c\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u4e2d\u7684Series<\/code>\u6216DataFrame<\/code>\u5bf9\u8c61\u6765\u8868\u793a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
      data = pd.Series([1, 2, 3, 5, 8, 13, 21, 34, 55])<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      3. \u8fdb\u884c\u5dee\u5206\u64cd\u4f5c<\/h4>\n<\/p>\n
      \u4f7f\u7528Pandas\u4e2d\u7684diff()<\/code>\u51fd\u6570\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\u3002diff()<\/code>\u51fd\u6570\u8ba1\u7b97\u76f8\u90bb\u5143\u7d20\u7684\u5dee\u503c\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684Series<\/code>\u5bf9\u8c61\u3002<\/p>\n<\/p>\n
      diff_data = data.diff(periods=1)<\/p>\n
      print(diff_data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      4. \u5dee\u5206\u7ed3\u679c\u5206\u6790<\/h4>\n<\/p>\n
      \u5dee\u5206\u64cd\u4f5c\u540e\u7684\u7ed3\u679c\u663e\u793a\u4e86\u76f8\u90bb\u5143\u7d20\u7684\u5dee\u503c\u3002\u901a\u8fc7\u89c2\u5bdf\u5dee\u5206\u7ed3\u679c\uff0c\u53ef\u4ee5\u5224\u65ad\u5e8f\u5217\u662f\u5426\u6d88\u9664\u4e86\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u53d8\u5f97\u66f4\u52a0\u7a33\u5b9a\u3002<\/p>\n<\/p>\n
      0     NaN<\/p>\n
      1     1.0<\/p>\n
      2     1.0<\/p>\n
      3     2.0<\/p>\n
      4     3.0<\/p>\n
      5     5.0<\/p>\n
      6     8.0<\/p>\n
      7    13.0<\/p>\n
      8    21.0<\/p>\n
      dtype: float64<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u56db\u3001\u5dee\u5206\u7684\u9006\u64cd\u4f5c<\/h3>\n<\/p>\n
      \u5dee\u5206\u64cd\u4f5c\u53ef\u4ee5\u6d88\u9664\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u4f46\u6709\u65f6\u9700\u8981\u6062\u590d\u539f\u59cb\u5e8f\u5217\u3002\u5dee\u5206\u7684\u9006\u64cd\u4f5c\u53ef\u4ee5\u901a\u8fc7\u7d2f\u79ef\u548c\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
      1. \u8fdb\u884c\u7d2f\u79ef\u548c\u64cd\u4f5c<\/h4>\n<\/p>\n
      \u4f7f\u7528Pandas\u4e2d\u7684cumsum()<\/code>\u51fd\u6570\u5bf9\u5dee\u5206\u540e\u7684\u5e8f\u5217\u8fdb\u884c\u7d2f\u79ef\u548c\u64cd\u4f5c\uff0c\u53ef\u4ee5\u6062\u590d\u539f\u59cb\u5e8f\u5217\u3002<\/p>\n<\/p>\n
      original_data = diff_data.cumsum()<\/p>\n
      print(original_data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2. \u6062\u590d\u539f\u59cb\u5e8f\u5217<\/h4>\n<\/p>\n
      \u7d2f\u79ef\u548c\u64cd\u4f5c\u540e\u7684\u7ed3\u679c\u662f\u4e00\u4e2a\u6062\u590d\u4e86\u539f\u59cb\u5e8f\u5217\u7684Series<\/code>\u5bf9\u8c61\u3002\u901a\u8fc7\u89c2\u5bdf\u6062\u590d\u540e\u7684\u5e8f\u5217\uff0c\u53ef\u4ee5\u9a8c\u8bc1\u5dee\u5206\u64cd\u4f5c\u7684\u6b63\u786e\u6027\u3002<\/p>\n<\/p>\n
      0     NaN<\/p>\n
      1     1.0<\/p>\n
      2     2.0<\/p>\n
      3     4.0<\/p>\n
      4     7.0<\/p>\n
      5    12.0<\/p>\n
      6    20.0<\/p>\n
      7    33.0<\/p>\n
      8    54.0<\/p>\n
      dtype: float64<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e94\u3001\u5dee\u5206\u7684\u5e94\u7528\u5b9e\u4f8b<\/h3>\n<\/p>\n
      \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5dee\u5206\u64cd\u4f5c\uff0c\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u5e94\u7528\u5b9e\u4f8b\u6765\u6f14\u793a\u5dee\u5206\u5728\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n
      1. \u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h4>\n<\/p>\n
      import pandas as pd<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2. \u52a0\u8f7d\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
      \u4f7f\u7528Pandas\u52a0\u8f7d\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u521d\u6b65\u7684\u53ef\u89c6\u5316\u5206\u6790\u3002<\/p>\n<\/p>\n
      data = pd.read_csv('time_series_data.csv')<\/p>\n
      data.plot()<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      3. \u8fdb\u884c\u5dee\u5206\u64cd\u4f5c<\/h4>\n<\/p>\n
      \u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u4e00\u9636\u5dee\u5206\u64cd\u4f5c\uff0c\u5e76\u8fdb\u884c\u53ef\u89c6\u5316\u5206\u6790\u3002<\/p>\n<\/p>\n
      diff_data = data.diff(periods=1)<\/p>\n
      diff_data.plot()<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      4. \u7a33\u5b9a\u6027\u68c0\u9a8c<\/h4>\n<\/p>\n
      \u901a\u8fc7\u89c2\u5bdf\u5dee\u5206\u540e\u7684\u5e8f\u5217\uff0c\u5224\u65ad\u5e8f\u5217\u662f\u5426\u6d88\u9664\u4e86\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u53d8\u5f97\u66f4\u52a0\u7a33\u5b9a\u3002<\/p>\n<\/p>\n
      from statsmodels.tsa.stattools import adfuller<\/p>\n
      result = adfuller(diff_data.dropna())<\/p>\n
      print('ADF Statistic:', result[0])<\/p>\n
      print('p-value:', result[1])<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      5. \u6a21\u578b\u6784\u5efa<\/h4>\n<\/p>\n
      \u5728\u5dee\u5206\u540e\u7684\u5e8f\u5217\u57fa\u7840\u4e0a\uff0c\u6784\u5efaARIMA\u6a21\u578b\uff0c\u5e76\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
      from statsmodels.tsa.arima.model import ARIMA<\/p>\n
      model = ARIMA(diff_data.dropna(), order=(1, 0, 1))<\/p>\n
      model_fit = model.fit()<\/p>\n
      print(model_fit.summary())<\/p>\n
      forecast = model_fit.forecast(steps=10)<\/p>\n
      print(forecast)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u516d\u3001\u5dee\u5206\u64cd\u4f5c\u7684\u6ce8\u610f\u4e8b\u9879<\/h3>\n<\/p>\n
      \u5728\u8fdb\u884c\u5dee\u5206\u64cd\u4f5c\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9\uff1a<\/p>\n<\/p>\n
        \n
      1. \n
        \u7f3a\u5931\u503c\u5904\u7406\uff1a<\/strong> \u5dee\u5206\u64cd\u4f5c\u4f1a\u5bfc\u81f4\u5e8f\u5217\u7684\u7b2c\u4e00\u4e2a\u5143\u7d20\u53d8\u4e3a\u7f3a\u5931\u503c\uff0c\u9700\u8981\u8fdb\u884c\u9002\u5f53\u7684\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u5dee\u5206\u6b21\u6570\u9009\u62e9\uff1a<\/strong> \u9009\u62e9\u9002\u5f53\u7684\u5dee\u5206\u6b21\u6570\uff0c\u4ee5\u6d88\u9664\u5e8f\u5217\u4e2d\u7684\u8d8b\u52bf\u6027\u6210\u5206\uff0c\u907f\u514d\u8fc7\u5ea6\u5dee\u5206\u3002<\/p>\n<\/p>\n<\/li>\n
      3. \n
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