Time Series Analysis with StatsModels#
This is the landing page for a tutorial on time series analysis, based on Chapter 12 of Think Stats, third edition.
Abstract
Time series analysis provides essential tools for modeling and predicting time-dependent data, especially data exhibiting seasonal patterns or serial correlation. This tutorial covers tools in the StatsModels library including seasonal decomposition and ARIMA. We’ll develop the ARIMA model bottom-up, implementing it one piece at a time, and then using StatsModels. As examples, we’ll look at weather data and electricity generation from renewable sources in the United States since 2004 – but the methods we’ll cover apply to many kinds of real-world time series data.
Slides for the PyData Global 2024 tutorial are here
The notebooks#
For each part of the tutorial, there are two notebook: the first contains blank cells for code-along activities and exercises; the second has all of the code and solutions to the exercises.
Part 1: Introduction and Seasonal Decomposition
Part 2: Seasonal Decomposition and Prediction
Part 3: Autocorrelation and ARIMA