1.5 Additional Resources

This online textbook is designed to be as self-contained as possible. Nevertheless, it is neither possible nor desirable to have a single work cover all aspects at all levels. The resources below contain additional background material and detailed derivations.

Recommended Textbook¶

For those looking to deepen their understandings of the mathematical and statistical underpinnings of time series analysis, I highly recommend Time Series Analysis and Its Applications by Robert Shumway and David Stoffer Shumway & Stoffer (2025). However, data scientists new to time series analysis may find Shumway and Stoffer to be too abstract and lacking in real world applications. For this reason, you may consider Shumway and Stoffer to be the next level beyond the material in this textbook.

Other Resources¶

There are many other valuable resources for learning time series analysis. I have found the following to be particularly useful:

1. Time Series Forecasting in Python¶

Author: Marco Peixeiro

In many ways the polar opposite of Shumway and Stoffer, Time Series Forecasting in Python contains multiple valuable guides to implementing time series analysis in Python but minimal statistical background.

2. Forecasting: Principles and Practice, 3rd ed.¶

Authors: Rob Hyndman and George Athanasopoulos
Available online: https://otexts.com/fpp3/, with a version incorporating Python recently published at https://otexts.com/fpppy/.

While the math and statistics are more basic than those covered in this textbook, it is still a valuable introduction to multiple topics, particularly exponential smoothing. The original versions used the language R, with a recent version using Python also available.

3. Prof. Steve Brunton’s YouTube Lectures¶

Topic: Fourier Analysis
Link: https://www.youtube.com/watch?v=jNC0jxb0OxE

An approachable but rigorous introduction to Fourier analysis.

4. Prof. Aric LaBarr’s YouTube Lessons¶

Topic: Time Series (5-minute lessons)
Link: https://www.youtube.com/@AricLaBarr/playlists

Easily understood short lessons on multiple topics in time series analysis and econometrics.

5. tsa4-python GitHub Repository¶

Link: https://github.com/borisgarbuzov/tsa4-python/tree/master

Ports the first three chapters of code from Time Series Analysis and Its Applications, 4th ed. from R to Python.

6. Kalman and Bayesian Filters in Python¶

Link: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

An online textbook with interactive notebooks providing intuitive explanations and demonstrations of Kalman filters.