Python中的时间序列分解函数 [英] Time Series Decomposition function in Python

问题描述

时间序列分解是一种将时间序列数据集分成三个(或更多)组件的方法.例如:

Time series decomposition is a method that separates a time-series data set into three (or more) components. For example:

x(t) = s(t) + m(t) + e(t)

哪里

t is the time coordinate
x is the data
s is the seasonal component
e is the random error term
m is the trend

在 RI 中将执行以下功能 分解和stl.我将如何在 python 中执行此操作?

In R I would do the functions decompose and stl. How would I do this in python?

推荐答案

我遇到了类似的问题，正在努力寻找最佳的前进道路.尝试将您的数据移动到 Pandas DataFrame 中，然后调用 StatsModels tsa.seasonal_decompose.请参阅以下示例:

I've been having a similar issue and am trying to find the best path forward. Try moving your data into a Pandas DataFrame and then call StatsModels tsa.seasonal_decompose. See the following example:

import statsmodels.api as sm

dta = sm.datasets.co2.load_pandas().data
# deal with missing values. see issue
dta.co2.interpolate(inplace=True)

res = sm.tsa.seasonal_decompose(dta.co2)
resplot = res.plot()

然后您可以从以下位置恢复分解的各个组件:

You can then recover the individual components of the decomposition from:

res.resid
res.seasonal
res.trend

我希望这会有所帮助！

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