season_decompose:操作数不能与序列中的形状一起广播 [英] seasonal_decompose: operands could not be broadcast together with shapes on a series

问题描述

我知道有关此主题的问题很多，但是没有一个问题帮助我解决了这个问题.我真的很坚持.

I know there are many questions on this topic, but none of them helped me to solve this problem. I'm really stuck on this.

一个简单的系列:

0
2016-01-31  266
2016-02-29  235
2016-03-31  347
2016-04-30  514
2016-05-31  374
2016-06-30  250
2016-07-31  441
2016-08-31  422
2016-09-30  323
2016-10-31  168
2016-11-30  496
2016-12-31  303

import statsmodels.api as sm
logdf = np.log(df[0])
decompose = sm.tsa.seasonal_decompose(logdf,freq=12, model='additive')
decomplot = decompose.plot()

我不断得到:ValueError: operands could not be broadcast together with shapes (12,) (14,)

我已经尝试了几乎所有内容，仅传递了logdf.values，传递了非日志系列.它不起作用. numpy和statsmodel版本:

I've tried pretty much everything, passing only logdf.values, passing a non-log series. It doesn't work. Numpy and statsmodel versions:

print(statsmodels.__version__)
print(pd.__version__)
print(np.__version__)
0.6.1
0.18.1
1.11.3

推荐答案

正如@yoonforh所指出的，在我的案例中，这是通过将freq参数设置为小于时间序列长度来解决的.例如.如果您的时间序列ts看起来像这样:

As @yoonforh pointed, in my case this was fixed by setting the freq parameter to less than the time series length. E.g. if your time series ts looks like this:

2014-01-01    0.0
2014-02-01    0.0
2014-03-01    1.0
2014-04-01    1.0
2014-05-01    0.0
2014-06-01    1.0
2014-07-01    1.0
2014-08-01    0.0
2014-09-01    0.0
2014-10-01    1.0
2014-11-01    0.0
2014-12-01    0.0

形状是

(12,)

因此这将导致上述错误:

so this will give the error as per above:

seasonal_decompose(ts, freq=12, model='additive')

但是如果我尝试freq=11或其他任何小于12的int，例如

but if I try freq=11 or any other int less than 12, e.g.

seasonal_decompose(ts, freq=11, model='additive')

这有效

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