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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) where
t is the time coordinate x is the data s is the seasonal component e is the random error term m is the trend In R I would do the functions decompose and stl. How would I do this in python?
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Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and noise components. Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting.
Image by Author. Time series decomposition is a technique that splits a time series into several components, each representing an underlying pattern category, trend, seasonality, and noise. In this tutorial, we will show you how to automatically decompose a time series with Python.
In Python, the statsmodels library has a seasonal_decompose() method that lets you decompose a time series into trend, seasonality and noise in one line of code.
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() 
You can then recover the individual components of the decomposition from:
res.resid res.seasonal res.trend I hope this helps!
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