Excellent piece (part one of three) about time series analysis by my colleague Carly Stambaugh

Recently, I was asked to determine the extent to which seasonality influenced a particular time series. No problem, right? The `statsmodels`

Python package has a `seasonal_decompose`

function that seemed pretty handy; and there’s always Google! As it turns out, this was a bit trickier than I expected. In this post I’ll share some of the problems I encountered while working on this project and how I solved them.

In attempting to find posts or papers that addressed quantifying the extent to which the time series was driven by seasonality, every example I came across fell into one of two categories:

- Here’s a few lines of code that produce a visualization of a time series decomposition.
- Here’s how you can remove the seasonality component of a time series, thus stabilizing your time series before building a predictive model.

Also, each example started with “Here’s a time series with a seasonal trend.”…

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