Hyperparameter Tuning for Time Series Causal Impact Analysis in Python | Machine Learning
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.
⏰ Timecodes ⏰
0:00 - Intro
0:15 - Step 1: Install and Import Libraries
1:10 - Step 2: Create Dataset
3:09 - Step 3: Set Pre and Post Periods
4:03 - Step 4: Causal Impact on Time Series with Default Hyperparameters
5:20 - Step 5: Hyperparameter Tuning for niter
5:48 - Step 6: Hyperparameter Tuning for standardize_data
6:15 - Step 7: Hyperparameter Tuning for prior_level_sd
7:49 - Step 8: Hyperparameter Tuning for nseasons
8:54 - Step 9: Hyperparameter Tuning for seasonal_duration
9:59 - Step 10: Hyperparameter Tuning for dynamic_regression
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