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我正在执行时间序列分析，并使用 Kwiatkowski–Phillips–Schmidt–Shin (KPSS) 检查平稳性。我使用以下方法加载了数据：

import pandas as pd
import numpy as np
path = 'https://raw.githubusercontent.com/selva86/datasets/master/daily-min-temperatures.csv'
df = pd.read_csv(path, parse_dates=['Date'], index_col='Date')
df.plot(title='Daily Temperatures', figsize=(14,8), legend=None);

这是我使用的代码，但我无法显示结果。

#define function for kpss test
from statsmodels.tsa.stattools import kpss
#define KPSS
def kpss_test(timeseries):
    print ('Results of KPSS Test:')
    kpsstest = kpss(timeseries, regression='c')
    kpss_output = pd.Series(kpsstest[0:3], index=['Test Statistic','p-value','Lags Used'])
    for key,value in kpsstest[3].items():
      kpss_output['Critical Value (%s)'%key] = value

助攻。

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1 回答 1

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你快到了，只需kpss_output像这样返回：

def kpss_test(timeseries):
    print ('Results of KPSS Test:')
    kpsstest = kpss(timeseries, regression='c')
    kpss_output = pd.Series(kpsstest[0:3], index=['Test Statistic','p-value','Lags Used'])
    for key,value in kpsstest[3].items():
      kpss_output['Critical Value (%s)'%key] = value
    
    return kpss_output

当你打电话时，kpss_test(df.Temp)你会得到：

Test Statistic            0.06511
p-value                   0.10000
Lags Used                30.00000
Critical Value (10%)      0.34700
Critical Value (5%)       0.46300
Critical Value (2.5%)     0.57400
Critical Value (1%)       0.73900
dtype: float64
于 2020-12-23T13:51:59.090 回答