{"id":232494,"date":"2024-05-11T22:41:52","date_gmt":"2024-05-11T14:41:52","guid":{"rendered":""},"modified":"2024-05-11T22:42:01","modified_gmt":"2024-05-11T14:42:01","slug":"%e6%9c%89%e8%b0%81%e7%9f%a5%e9%81%93gsadf%e6%a3%80%e9%aa%8c%e5%8e%9f%e7%90%86%e5%90%97%ef%bc%8c%e6%80%8e%e4%b9%88%e7%bc%96%e5%86%99%e4%bb%a3%e7%a0%81","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/232494.html","title":{"rendered":"\u6709\u8c01\u77e5\u9053GSADF\u68c0\u9a8c\u539f\u7406\u5417\uff0c\u600e\u4e48\u7f16\u5199\u4ee3\u7801"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25154649\/b6e9645a-d55d-4646-9166-85e989bc6962.webp\" alt=\"\u6709\u8c01\u77e5\u9053GSADF\u68c0\u9a8c\u539f\u7406\u5417\uff0c\u600e\u4e48\u7f16\u5199\u4ee3\u7801\" \/><\/p>\n<p><p>GSADF\uff08Generalized Sup Augmented Dickey-Fuller\uff09\u68c0\u9a8c\u662f\u4e00\u79cd\u7528\u4e8e\u786e\u5b9a\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u662f\u5426\u5b58\u5728\u5355\u4f4d\u6839\uff0c\u4ece\u800c\u5224\u65ad\u5e8f\u5217\u662f\u5426\u7a33\u5b9a\u7684\u7edf\u8ba1\u65b9\u6cd5\u3002\u7b80\u800c\u8a00\u4e4b\uff0c<strong>\u5b83\u662f\u7528\u6765\u68c0\u9a8c\u65f6\u95f4\u5e8f\u5217\u662f\u5426\u5177\u6709\u7206\u70b8\u6027\u6839\u7684\u5f3a\u5316\u7248\u672cADF\u68c0\u9a8c\u3001\u9002\u7528\u4e8e\u91d1\u878d\u65f6\u5e8f\u6570\u636e\u5206\u6790<\/strong>\u3002\u5728GSADF\u68c0\u9a8c\u4e2d\uff0c\u5173\u952e\u5728\u4e8e\u80fd\u591f\u901a\u8fc7\u6269\u5c55\u7684\u7a97\u53e3\u65b9\u6cd5\uff0c\u66f4\u7075\u6d3b\u5730\u8bc6\u522b\u51fa\u65f6\u95f4\u5e8f\u5217\u5185\u7684\u591a\u4e2a\u4e0d\u7a33\u5b9a\u5b50\u5e8f\u5217\uff0c\u8fd9\u6bd4\u4f20\u7edfADF\u68c0\u9a8c\u66f4\u52a0\u7cbe\u51c6\u3001\u7075\u654f\u3002\u901a\u8fc7\u6539\u53d8\u68c0\u9a8c\u7684\u7a97\u53e3\u5927\u5c0f\uff0cGSADF\u80fd\u591f\u68c0\u6d4b\u5230\u65f6\u95f4\u5e8f\u5217\u5728\u4e0d\u540c\u65f6\u95f4\u6bb5\u7684\u7206\u70b8\u6027\u884c\u4e3a\uff0c\u8fd9\u5927\u5927\u589e\u5f3a\u4e86\u5176\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u5b9e\u7528\u6027\uff0c\u5c24\u5176\u5728\u5206\u6790\u91d1\u878d\u5e02\u573a\u7684\u6ce1\u6cab\u548c\u574d\u584c\u65f6\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001GSADF\u68c0\u9a8c\u539f\u7406<\/h3>\n<\/p>\n<p><p>GSADF\u68c0\u9a8c\u7684\u6838\u5fc3\u539f\u7406\u662f\u6269\u5c55\u4e86ADF\u68c0\u9a8c\u7684\u6982\u5ff5\uff0c\u5c06\u4f20\u7edf\u7684\u5355\u4e00\u6837\u672cADF\u68c0\u9a8c\u6269\u5c55\u5230\u5141\u8bb8\u591a\u4e2a\u6837\u672c\u7a97\u53e3\u8fdb\u884c\u68c0\u9a8c\u3002\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u5728\u65f6\u95f4\u5e8f\u5217\u7684\u4e0d\u540c\u5b50\u5e8f\u5217\u4e2d\u68c0\u6d4b\u5355\u4f4d\u6839\u7684\u5b58\u5728\u6027\uff0c\u4ece\u800c\u66f4\u5168\u9762\u5730\u8bc4\u4f30\u65f6\u95f4\u5e8f\u5217\u7684\u7a33\u5b9a\u6027\u3002<\/p>\n<\/p>\n<p><h4>GSADF\u68c0\u9a8c\u7684\u6b65\u9aa4\uff1a<\/h4>\n<\/p>\n<ol>\n<li>\u786e\u5b9a\u68c0\u9a8c\u7684\u6ed1\u52a8\u7a97\u53e3\u5927\u5c0f\u3002\u8fd9\u662fGSADF\u68c0\u9a8c\u4e2d\u7684\u4e00\u4e2a\u5173\u952e\u53c2\u6570\uff0c\u5f71\u54cd\u5230\u68c0\u9a8c\u7684\u7075\u654f\u5ea6\u548c\u51c6\u786e\u6027\u3002<\/li>\n<li>\u5728\u786e\u5b9a\u7684\u6ed1\u52a8\u7a97\u53e3\u5185\uff0c\u5bf9\u6bcf\u4e2a\u7a97\u53e3\u8fdb\u884cADF\u5355\u4f4d\u6839\u68c0\u9a8c\u3002\u901a\u8fc7\u9010\u6b65\u79fb\u52a8\u7a97\u53e3\uff0c\u91cd\u590d\u6b64\u6b65\u9aa4\u76f4\u81f3\u8986\u76d6\u6574\u4e2a\u65f6\u95f4\u5e8f\u5217\u3002<\/li>\n<li>\u7efc\u5408\u5404\u4e2a\u7a97\u53e3\u7684ADF\u68c0\u9a8c\u7ed3\u679c\uff0c\u5224\u65ad\u6574\u4e2a\u65f6\u95f4\u5e8f\u5217\u662f\u5426\u5b58\u5728\u5355\u4f4d\u6839\u3002<\/li>\n<\/ol>\n<p><h4>\u4f18\u52bf\u548c\u5c40\u9650\uff1a<\/h4>\n<\/p>\n<ul>\n<li><strong>\u4f18\u52bf<\/strong>\uff1aGSADF\u68c0\u9a8c\u80fd\u591f\u66f4\u7cbe\u51c6\u5730\u68c0\u6d4b\u5230\u65f6\u95f4\u5e8f\u5217\u7684\u4e0d\u7a33\u5b9a\u6027\uff0c\u5c24\u5176\u662f\u5728\u5206\u6790\u5177\u6709\u591a\u4e2a\u4e0d\u7a33\u5b9a\u5468\u671f\u6216\u6ce1\u6cab\u7684\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u65f6\uff0c\u5176\u4f18\u52bf\u660e\u663e\u3002<\/li>\n<li>\u5c40\u9650\uff1aGSADF\u68c0\u9a8c\u76f8\u8f83\u4e8eADF\u68c0\u9a8c\u800c\u8a00\uff0c\u8ba1\u7b97\u91cf\u66f4\u5927\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\u66f4\u4e3a\u660e\u663e\u3002\u56e0\u6b64\uff0c\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u9700\u8981\u6743\u8861\u68c0\u9a8c\u7684\u7cbe\u51c6\u5ea6\u548c\u8ba1\u7b97\u8d44\u6e90\u7684\u6d88\u8017\u3002<\/li>\n<\/ul>\n<p><h3>\u4e8c\u3001GSADF\u68c0\u9a8c\u4ee3\u7801\u5b9e\u73b0<\/h3>\n<\/p>\n<p><p>\u5728\u7f16\u5199GSADF\u68c0\u9a8c\u7684\u4ee3\u7801\u65f6\uff0c\u6211\u4eec\u901a\u5e38\u4f9d\u8d56\u4e8e\u4e00\u4e9b\u7edf\u8ba1\u548c\u79d1\u5b66\u8ba1\u7b97\u7684Python\u5e93\uff0c\u5982statsmodels\u548cnumpy\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684GSADF\u68c0\u9a8c\u4ee3\u7801\u793a\u4f8b\uff0c\u65e8\u5728\u4f7f\u7528Python\u5b9e\u73b0GSADF\u68c0\u9a8c\u7684\u57fa\u672c\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n<p><h4>\u5bfc\u5165\u6240\u9700\u5e93<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>import pandas as pd<\/p>\n<p>from statsmodels.tsa.stattools import adfuller<\/p>\n<p>from statsmodels.tsa.stattools import adfuller as ADF<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>GSADF\u68c0\u9a8c\u51fd\u6570\u5b9a\u4e49<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\">def GSADF_test(timeseries, max_lag=None, window_size=0.1):<\/p>\n<p>    &quot;&quot;&quot;<\/p>\n<p>    GSADF\u68c0\u9a8c\u51fd\u6570<\/p>\n<p>    timeseries: \u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/p>\n<p>    max_lag: ADF\u68c0\u9a8c\u7684\u6700\u5927\u6ede\u540e\u9636\u6570<\/p>\n<p>    window_size: \u6ed1\u52a8\u7a97\u53e3\u7684\u5927\u5c0f\uff0c\u8868\u793a\u4e3a\u65f6\u95f4\u5e8f\u5217\u603b\u957f\u5ea6\u7684\u6bd4\u4f8b<\/p>\n<p>    &quot;&quot;&quot;<\/p>\n<p>    if isinstance(window_size, float):<\/p>\n<p>        window_size = int(len(timeseries) * window_size)<\/p>\n<p>    n = len(timeseries)<\/p>\n<p>    adf_values = []<\/p>\n<p>    for start in range(n - window_size + 1):<\/p>\n<p>        end = start + window_size<\/p>\n<p>        window_series = timeseries[start:end]<\/p>\n<p>        adf_result = ADF(window_series, max_lag=max_lag)<\/p>\n<p>        adf_values.append(adf_result[0])  # \u53ea\u6536\u96c6\u6bcf\u4e2a\u7a97\u53e3\u7684ADF\u7edf\u8ba1\u91cf<\/p>\n<p>    min_adf = np.min(adf_values)  # \u627e\u5230\u6700\u5c0f\u7684ADF\u7edf\u8ba1\u91cf\u503c<\/p>\n<p>    return min_adf, adf_values<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h4>\u5e94\u7528GSADF\u68c0\u9a8c<\/h4>\n<\/p>\n<p><pre><code class=\"language-python\"># \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636edata<\/p>\n<p>data = pd.read_csv(&#039;your_data_file.csv&#039;)  # \u52a0\u8f7d\u6570\u636e<\/p>\n<p>timeseries = data[&#039;your_timeseries_column&#039;]  # \u63d0\u53d6\u65f6\u95f4\u5e8f\u5217\u5217<\/p>\n<h2><strong>\u6267\u884cGSADF\u68c0\u9a8c<\/strong><\/h2>\n<p>min_adf, adf_values = GSADF_test(timeseries)<\/p>\n<p>print(&quot;GSADF\u68c0\u9a8c\u7684\u6700\u5c0fADF\u7edf\u8ba1\u503c: &quot;, min_adf)<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001GSADF\u68c0\u9a8c\u5728\u91d1\u878d\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/h3>\n<\/p>\n<p><h4>\u5e94\u7528\u80cc\u666f<\/h4>\n<\/p>\n<p><p>GSADF\u68c0\u9a8c\u5728\u91d1\u878d\u6570\u636e\u5206\u6790\u4e2d\u5c24\u4e3a\u91cd\u8981\uff0c\u539f\u56e0\u5728\u4e8e\u91d1\u878d\u5e02\u573a\u5e38\u5e38\u51fa\u73b0\u6ce1\u6cab\u548c\u7834\u706d\u73b0\u8c61\uff0c\u8fd9\u4e9b\u73b0\u8c61\u5728\u65f6\u95f4\u5e8f\u5217\u4e2d\u4f53\u73b0\u4e3a\u4e0d\u7a33\u5b9a\u7684\u7206\u70b8\u6027\u6839\u3002\u901a\u8fc7GSADF\u68c0\u9a8c\uff0c\u6211\u4eec\u53ef\u4ee5\u6709\u6548\u5730\u8bc6\u522b\u548c\u5206\u6790\u8fd9\u4e9b\u7279\u5f81\uff0c\u4e3a\u91d1\u878d\u5e02\u573a\u7684\u7814\u7a76\u548c\u9884\u6d4b\u63d0\u4f9b\u5f3a\u6709\u529b\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n<p><h4>\u6848\u4f8b\u5206\u6790<\/h4>\n<\/p>\n<p><p>\u4ee5\u80a1\u7968\u5e02\u573a\u4e3a\u4f8b\uff0c\u5e94\u7528GSADF\u68c0\u9a8c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u51fa\u80a1\u4ef7\u65f6\u95f4\u5e8f\u5217\u4e2d\u7684\u5f02\u5e38\u6ce2\u52a8\u533a\u57df\uff0c\u4ece\u800c\u9884\u8b66\u6f5c\u5728\u7684\u91d1\u878d\u6ce1\u6cab\u3002\u901a\u8fc7\u5728\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u5185\u68c0\u6d4bADF\u503c\uff0cGSADF\u68c0\u9a8c\u80fd\u591f\u63ed\u793a\u51fa\u80a1\u4ef7\u5728\u7279\u5b9a\u65f6\u671f\u5185\u7684\u975e\u7a33\u5b9a\u6027\uff0c\u5bf9\u4e8e\u6295\u8d44\u51b3\u7b56\u548c\u98ce\u9669\u7ba1\u7406\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002<\/p>\n<\/p>\n<p><h3>\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n<p><p>GSADF\u68c0\u9a8c\u4f5c\u4e3a\u4e00\u79cd\u5148\u8fdb\u7684\u65f6\u95f4\u5e8f\u5217\u7a33\u5b9a\u6027\u68c0\u9a8c\u65b9\u6cd5\uff0c<strong>\u5f25\u8865\u4e86\u4f20\u7edfADF\u68c0\u9a8c\u5728\u5904\u7406\u590d\u6742\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u65f6\u7684\u4e0d\u8db3<\/strong>\u3002\u901a\u8fc7\u63d0\u4f9b\u5bf9\u65f6\u95f4\u5e8f\u5217\u4e0d\u7a33\u5b9a\u6027\u7684\u66f4\u7ec6\u81f4\u3001\u6df1\u5165\u7684\u5206\u6790\uff0cGSADF\u68c0\u9a8c\u4e3a\u91d1\u878d\u5e02\u573a\u5206\u6790\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u3002\u5c3d\u7ba1\u5728\u5b9e\u8df5\u4e2d\u9700\u8981\u8f83\u5927\u7684\u8ba1\u7b97\u8d44\u6e90\uff0c\u4f46\u5176\u5728\u63ed\u793a\u91d1\u878d\u5e02\u573a\u52a8\u6001\u3001\u9884\u6d4b\u5e02\u573a\u8d8b\u52bf\u65b9\u9762\u7684\u4ef7\u503c\u662f\u4e0d\u53ef\u5ffd\u89c6\u7684\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p><strong>Q: GSADF\u68c0\u9a8c\u539f\u7406\u662f\u4ec0\u4e48\uff1f\u5b83\u9002\u7528\u4e8e\u54ea\u4e9b\u573a\u666f\uff1f<\/strong><\/p>\n<p>A: GSADF\uff08Generalized Sequential AutoRegressive Conditional Heteroskedasticity test with Dominance)\u662f\u4e00\u79cd\u5e7f\u4e49\u987a\u5e8f\u81ea\u56de\u5f52\u6761\u4ef6\u5f02\u65b9\u5dee\u6027\u68c0\u9a8c\u65b9\u6cd5\uff0c\u7528\u4e8e\u5206\u6790\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4e2d\u7684\u6761\u4ef6\u5f02\u65b9\u5dee\u6027\u3002\u5b83\u662f\u9488\u5bf9ARCH\u6548\u5e94\u7684\u53d8\u4f53\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u68c0\u6d4b\u91d1\u878d\u5e02\u573a\u548c\u7ecf\u6d4e\u9886\u57df\u4e2d\u7684\u6ce2\u52a8\u6027\u53d8\u5316\u3002GSADF\u68c0\u9a8c\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u5b58\u5728\u5f02\u65b9\u5dee\u6027\u7684\u6570\u636e\uff0c\u5e76\u63d0\u4f9b\u4e86\u7edf\u8ba1\u5b66\u4e0a\u7684\u4f9d\u636e\u6765\u5bf9\u5176\u8fdb\u884c\u63a8\u65ad\u548c\u5206\u6790\u3002<\/p>\n<p><strong>Q: \u5982\u4f55\u7f16\u5199GSADF\u68c0\u9a8c\u7684\u4ee3\u7801\uff1f\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u53ef\u4ee5\u5b9e\u73b0\uff1f<\/strong><\/p>\n<p>A: \u7f16\u5199GSADF\u68c0\u9a8c\u7684\u4ee3\u7801\u9700\u8981\u9996\u5148\u4e86\u89e3\u8be5\u65b9\u6cd5\u7684\u6570\u5b66\u539f\u7406\uff0c\u5e76\u9009\u62e9\u5408\u9002\u7684\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u8f6f\u4ef6\u6216\u7f16\u7a0b\u8bed\u8a00\u6765\u5b9e\u73b0\u3002\u5e38\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u5305\u62ecR\u3001Python\u548cMATLAB\uff0c\u5b83\u4eec\u90fd\u6709\u76f8\u5e94\u7684\u7edf\u8ba1\u5206\u6790\u5e93\u548c\u5de5\u5177\u4f9b\u4f7f\u7528\u3002<\/p>\n<p>\u5728R\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528rugarch\u5305\u6765\u8fdb\u884cGSADF\u68c0\u9a8c\u7684\u8ba1\u7b97\uff0c\u8be5\u5305\u5177\u6709\u5f3a\u5927\u7684\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u529f\u80fd\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528statsmodels\u5e93\u4e2d\u7684ARCH\u6a21\u578b\u8fdb\u884cGSADF\u68c0\u9a8c\u7684\u5b9e\u73b0\uff0c\u8be5\u5e93\u4e5f\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u6a21\u578b\u548c\u65b9\u6cd5\u3002\u5728MATLAB\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528econometrics\u5de5\u5177\u7bb1\u4e2d\u7684ARCH\/GARCH\u6a21\u578b\u8fdb\u884cGSADF\u68c0\u9a8c\u7684\u8ba1\u7b97\u3002<\/p>\n<p>\u7f16\u5199\u4ee3\u7801\u65f6\uff0c\u9700\u8981\u6ce8\u610f\u6570\u636e\u7684\u9884\u5904\u7406\u3001\u8bbe\u7f6e\u6a21\u578b\u53c2\u6570\u3001\u8ba1\u7b97\u68c0\u9a8c\u7edf\u8ba1\u91cf\u3001\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u4ee5\u53ca\u7ed3\u679c\u7684\u89e3\u91ca\u7b49\u6b65\u9aa4\u3002\u7f16\u5199\u4ee3\u7801\u524d\u5efa\u8bae\u5148\u719f\u6089\u76f8\u5173\u7684\u7edf\u8ba1\u5b66\u77e5\u8bc6\u548c\u7f16\u7a0b\u8bed\u8a00\u7684\u57fa\u7840\u7528\u6cd5\uff0c\u4ee5\u53ca\u9605\u8bfb\u76f8\u5e94\u8f6f\u4ef6\u6216\u5e93\u7684\u6587\u6863\u548c\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<p><strong>Q: GSADF\u68c0\u9a8c\u6709\u54ea\u4e9b\u5e94\u7528\u9886\u57df\uff1f\u5b83\u5bf9\u91d1\u878d\u5e02\u573a\u548c\u7ecf\u6d4e\u5206\u6790\u6709\u4f55\u5f71\u54cd\uff1f<\/strong><\/p>\n<p>A: GSADF\u68c0\u9a8c\u65b9\u6cd5\u5728\u91d1\u878d\u5e02\u573a\u548c\u7ecf\u6d4e\u9886\u57df\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u5b83\u53ef\u4ee5\u7528\u4e8e\u7814\u7a76\u80a1\u7968\u4ef7\u683c\u3001\u6c47\u7387\u3001\u5546\u54c1\u4ef7\u683c\u7b49\u91d1\u878d\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u6ce2\u52a8\u6027\u53d8\u5316\u3002\u5177\u4f53\u5e94\u7528\u5305\u62ec\u91d1\u878d\u98ce\u9669\u7ba1\u7406\u3001\u6295\u8d44\u7ec4\u5408\u4f18\u5316\u3001\u671f\u6743\u5b9a\u4ef7\u3001\u91d1\u878d\u5e02\u573a\u9884\u6d4b\u7b49\u65b9\u9762\u3002<\/p>\n<p>GSADF\u68c0\u9a8c\u80fd\u591f\u5e2e\u52a9\u8bc6\u522b\u5b58\u5728\u6761\u4ef6\u5f02\u65b9\u5dee\u6027\u7684\u6570\u636e\uff0c\u63ed\u793a\u4e86\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4e2d\u66f4\u52a0\u6df1\u5c42\u6b21\u7684\u6ce2\u52a8\u6027\u7279\u5f81\u3002\u901a\u8fc7\u5bf9\u91d1\u878d\u5e02\u573a\u548c\u7ecf\u6d4e\u6570\u636e\u7684\u6ce2\u52a8\u6027\u53d8\u5316\u8fdb\u884c\u68c0\u9a8c\u548c\u5206\u6790\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u5e02\u573a\u72b6\u51b5\u548c\u8d8b\u52bf\uff0c\u4e3a\u51b3\u7b56\u5236\u5b9a\u63d0\u4f9b\u6709\u529b\u652f\u6301\u3002\u6b64\u5916\uff0cGSADF\u68c0\u9a8c\u8fd8\u53ef\u4ee5\u4e3a\u91d1\u878d\u98ce\u9669\u7ba1\u7406\u548c\u6295\u8d44\u7b56\u7565\u7684\u5236\u5b9a\u63d0\u4f9b\u79d1\u5b66\u4f9d\u636e\uff0c\u5e2e\u52a9\u6295\u8d44\u8005\u66f4\u52a0\u51c6\u786e\u5730\u8bc4\u4f30\u98ce\u9669\u548c\u6536\u76ca\u7684\u6f5c\u5728\u5173\u7cfb\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"GSADF\uff08Generalized Sup Augmented Dickey-Fuller\uff09\u68c0\u9a8c\u662f\u4e00\u79cd\u7528\u4e8e\u786e\u5b9a [&hellip;]","protected":false},"author":3,"featured_media":232511,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/232494"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=232494"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/232494\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/232511"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=232494"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=232494"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=232494"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}