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.”…
View original post 1,099 more words