我已经回答了这个问题here，但是下面是一个关于如何使用rpy2实现这个功能的快速函数。这使您能够对黄土使用R的健壮统计分解，但是在python中！import pandas as pd

from rpy2.robjects import r, pandas2ri

import numpy as np

from rpy2.robjects.packages import importr

def decompose(series, frequency, s_window = 'periodic', log = False, **kwargs):

'''

Decompose a time series into seasonal, trend and irregular components using loess,

acronym STL.

https://www.rdocumentation.org/packages/stats/versions/3.4.3/topics/stl

params:

series: a time series

frequency: the number of observations per “cycle”

(normally a year, but sometimes a week, a day or an hour)

https://robjhyndman.com/hyndsight/seasonal-periods/

s_window: either the character string "periodic" or the span

(in lags) of the loess window for seasonal extraction,

which should be odd and at least 7, according to Cleveland

et al.

log: boolean. take log of series

**kwargs: See other params for stl at

https://www.rdocumentation.org/packages/stats/versions/3.4.3/topics/stl

'''

df = pd.DataFrame()

df['date'] = series.index

if log: series = series.pipe(np.log)

s = [x for x in series.values]

length = len(series)

s = r.ts(s, frequency=frequency)

decomposed = [x for x in r.stl(s, s_window).rx2('time.series')]

df['observed'] = series.values

df['trend'] = decomposed[length:2*length]

df['seasonal'] = decomposed[0:length]

df['residuals'] = decomposed[2*length:3*length]

return df

上面的函数假设序列具有日期时间索引。它返回一个包含各个组件的数据帧，然后您可以使用自己喜爱的图形库绘制这些组件的图形。

可以传递stl seen here的参数，但可以将任何句点更改为下划线，例如，上面函数中的位置参数是súu window，但在上面的链接中是s.window。另外，我在this repository上找到了上面的一些代码。

示例数据

希望下面的工作，老实说，没有尝试过，因为这是一个要求后，我回答了问题很久。import pandas as pd

import numpy as np

obs_per_cycle = 52

observations = obs_per_cycle * 3

data = [v+2*i for i,v in enumerate(np.random.normal(5, 1, observations))]

tidx = pd.date_range('2016-07-01', periods=observations, freq='w')

ts = pd.Series(data=data, index=tidx)

df = decompose(ts, frequency=obs_per_cycle, s_window = 'periodic')