seasonal_decompose()要求freq是作为DateTimeIndex元信息的一部分提供的，可以pandas.Index.inferred_freq由用户推断，也可以由用户推断为，integer它给出每个周期的周期数。例如，每月12次（从docstring到seasonal_mean）：
def seasonal_decompose(x, model="additive", filt=None, freq=None):
"""
Parameters
----------
x : array-like
Time series
model : str {"additive", "multiplicative"}
Type of seasonal component. Abbreviations are accepted.
filt : array-like
The filter coefficients for filtering out the seasonal
component. The default is a symmetric moving average. freq : int, optional Frequency of the series. Must be used if x is not a pandas object with a timeseries index.
为了说明-使用随机样本数据：
length = 400
x = np.sin(np.arange(length)) * 10 + np.random.randn(length)
df = pd.DataFrame(data=x, index=pd.date_range(start=datetime(2015, 1, 1), periods=length, freq='w'), columns=['value'])
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 400 entries, 2015-01-04 to 2022-08-28
Freq: W-SUN
decomp = sm.tsa.seasonal_decompose(df)
data = pd.concat([df, decomp.trend, decomp.seasonal, decomp.resid], axis=1)
data.columns = ['series', 'trend', 'seasonal', 'resid']
Data columns (total 4 columns):
series 400 non-null float64
trend 348 non-null float64
seasonal 400 non-null float64
resid 348 non-null float64
dtypes: float64(4)
memory usage: 15.6 KB
到目前为止，一切都很好-现在从中随机删除元素DateTimeIndex以创建空间不均匀的数据：
df = df.iloc[np.unique(np.random.randint(low=0, high=length, size=length * .8))]
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 222 entries, 2015-01-11 to 2022-08-21
Data columns (total 1 columns):
value 222 non-null float64
dtypes: float64(1)
memory usage: 3.5 KB
df.index.freq
None
df.index.inferred_freq
None
seasonal_decomp在此数据上运行“有效”：
``` decomp = sm.tsa.seasonal_decompose(df, freq=52)
data = pd.concat([df, decomp.trend, decomp.seasonal, decomp.resid], axis=1)
data.columns = ['series', 'trend', 'seasonal', 'resid']
DatetimeIndex: 224 entries, 2015-01-04 to 2022-08-07
Data columns (total 4 columns):
series 224 non-null float64
trend 172 non-null float64
seasonal 224 non-null float64
resid 172 non-null float64
dtypes: float64(4)
memory usage: 8.8 KB
```
问题是- 结果有多有用。即使数据之间没有缺口，也无法使季节模式的推断复杂化（请参阅发行说明.interpolate()中的示例使用，也可以使此过程符合以下条件：statsmodels
Notes
-----
This is a naive decomposition. More sophisticated methods should
be preferred.
The additive model is Y[t] = T[t] + S[t] + e[t]
The multiplicative model is Y[t] = T[t] * S[t] * e[t]
The seasonal component is first removed by applying a convolution
filter to the data. The average of this smoothed series for each
period is the returned seasonal component.
602392714
清零编程群