我在 python 中分解时间序列时遇到问题

I'm having trouble to decompose a time-series, in python

所以我使用了之前的答案和问题来解决我的问题answer,但在我的情况下,我遇到了一些错误,我不知道如何解决它。

最初我将 pandas 数据框加载为 df = pd.read_excel(fid_data),其内容在下一个命令 df.info() 中检查,我得到以下内容:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 8 columns):
Date       118 non-null datetime64[ns]
MOEX       118 non-null float64
RTS        118 non-null float64
CAC40      118 non-null float64
DAX        118 non-null float64
FTSe100    118 non-null float64
nikkei     118 non-null float64
sp500      118 non-null float64
dtypes: datetime64[ns](1), float64(7)
memory usage: 7.5 KB

当我尝试使用此命令 res = sm.tsa.seasonal_decompose(moex, model='additive') 分解 moex = df.MOEX 时,出现以下错误:

Traceback (most recent call last):
  File "Main.py", line 106, in <module>
    res = sm.tsa.seasonal_decompose(moex, model='additive')
  File "/home/arvaldez/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/seasonal.py", line 68, in seasonal_decompose
    _pandas_wrapper, pfreq = _maybe_get_pandas_wrapper_freq(x)
  File "/home/arvaldez/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/filters/_utils.py", line 46, in _maybe_get_pandas_wrapper_freq
    freq = index.inferred_freq
AttributeError: 'RangeIndex' object has no attribute 'inferred_freq'

非常感谢@QuangHoang,在加载 pandas df 对象后,您必须使用 df.set_index('Date', inplace=True) 定义时间尺度,并且变量定义现在不包含 Date数组。

之前:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 8 columns):
Date       118 non-null datetime64[ns]
MOEX       118 non-null float64
RTS        118 non-null float64
CAC40      118 non-null float64
DAX        118 non-null float64
FTSe100    118 non-null float64
nikkei     118 non-null float64
sp500      118 non-null float64
dtypes: datetime64[ns](1), float64(7)
memory usage: 7.5 KB

之后:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 118 entries, 2019-02-01 to 2009-05-01
Data columns (total 7 columns):
MOEX       118 non-null float64
RTS        118 non-null float64
CAC40      118 non-null float64
DAX        118 non-null float64
FTSe100    118 non-null float64
nikkei     118 non-null float64
sp500      118 non-null float64
dtypes: float64(7)
memory usage: 7.4 KB

一切正常。现在我不需要解析 Date 数组,因为它已插入到每个数组中...

再次感谢。-