季节性运行时季节始终是7。这是为什么？
我一直在大约20个完全不同的数据集中从统计模型中运行pesional_decompose（）。在看日期频率的数据集时，季节性是7吗？
这是一个图片作为一个数据集分解的示例。我放大了季节性，以便您可以看到它再次是7天：
为什么总是7天？我不会期望它总是7天，并且数据集彼此之间的不同，所以到现在为止，我认为这是完全巧合，或者这是因为sensional_decompose（）。
但是，查看统计模型文档中的时令_decostose（），它使用loess来找出季节性。如果我看公式，它应该能够找到季节性的不同频率。我只需要验证我在这里没有错：我所有的数据集都产生7天的季节性频率是纯粹的巧合吗？
I have been running seasonal_decompose() from the statsmodels on about 20 totally different datasets. Is it standard that the seasonality is 7 when looking at a dataset with day frequency?
Here is a picture as an example of one dataset decomp. I zoomed in on the seasonality so that you can see that it is again 7 days:
Why is it always 7 days though? I wouldn't expect it to be always 7 days and the datasets are all different from each other, so by now I think that either this is total coincidence or this is because of seasonal_decompose().
But looking at how seasonal_decompose() in the statsmodels documentation , it uses LOESS to figure out the seasonality. If I look at the formula, it should be able to find different frequencies of the seasonality. I just need to verify that I am not wrong here: Is it pure coincidence that all of my datasets produce a 7 day frequency of the seasonality?
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首先，
pensional_decompose与黄土无关，对于基于loess的分解，您需要使用statsmodels.tsa.tsa.seasonal.stl。pensional_decostose均不会以任何方式根据数据推断周期性。您只有两个选项：期间参数ofientatnone的参数。在这种情况下，您必须将pandas带有DateTime索引的DataFramesensional_decostose，并且会从DateTime索引频率标签中推断出周期性，否则会丢弃错误。它首先获取频率标签：pfreq = getAttr（getAttr（x，“ index”，none），“ peashred_freq”，none）（在您的情况下，频率标签将为'd'，意思是每天），然后使用将转换为<statsmodels.tsa.tsatools.freq_to_period'd'd'> 7，这将用作周期性，因此您获得的结果）First of all,
seasonal_decomposehas nothing to do with LOESS, for decomposition based on LOESS you need to usestatsmodels.tsa.seasonal.STL.seasonal_decomposedoes not infer periodicity based on data in any way. You only have two options:periodargumentperiodargument atNone. In this case you have to feedpandasdataframe with datetime index toseasonal_decompose, and periodicity will be inferred from datetime index frequency label, otherwise it will throw an error. It first fetches frequency label:pfreq = getattr(getattr(x, "index", None), "inferred_freq", None)(in your case frequency label will be'D', meaning daily), then it converts it to periodicity usingstatsmodels.tsa.tsatools.freq_to_period(in your case frequency label'D'will be converted to7, and that will be used as periodicity, hence the results you get)