11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)
Last Updated on August 20, 2020
Machine learning methods can be used for classification and forecasting on time series problems.
Before exploring machine learning methods for time series, it is a good idea to ensure you have exhausted classical linear time series forecasting methods. Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform well on a wide range of problems, assuming that your data is suitably prepared and the method is well configured.
In this post, will you will discover a suite of classical methods for time series forecasting that you can test on your forecasting problem prior to exploring to machine learning methods.
The post is structured as a cheat sheet to give you just enough information on each method to get started with a working code example and where to look to get more information on the method.
All code examples are in Python and use the Statsmodels library. The APIs for this library can be tricky for beginners (trust me!), so having a working code example as a starting point will greatly accelerate your progress.
This is a large post; you may want to bookmark it.