Hyperparameter Tuning for Time Series Causal Impact Analysis in Python

Hyperparameter tuning for Google’s Python package CausalImpact on time series intervention with Bayesian Structural Time Series Model (BSTS)

Photo by noor Younis on Unsplash

CausalImpact package created by Google estimates the impact of an intervention on a time series. In this tutorial, we will talk about how to tune the hyperparameters of the time series causal impact model using the python package CausalImpact.

Resources for this post:

• Video tutorial for this post on YouTube
• Click here for the Colab notebook
• More video tutorials on Causal Inference and Time Series
• More blog posts on Causal Inference and Time Series
• If you are not a Medium member and want to support me as a writer to keep creating free content (😄 Buy me a cup of coffee ☕), join Medium membership through this link. You will get full access to posts on Medium for $5 per month, and I will receive a portion of it. Thank you for your support!

Let’s get started!

Step 1: Install and Import Libraries

In step 1, we will install and import the python libraries.