Summary:

The AutoGluon time-series module has proven to be a powerful tool for forecasting tasks. However, one area that could significantly enhance its utility is the inclusion of feature importance explainability in terms of both global training as well as inclusion as covariates, akin to what is currently available in the AutoGluon tabular module. This feature would greatly aid in understanding model decisions, facilitating a more intuitive analysis and improvement of models by highlighting which features contribute most to predictions.

Detail:

The tabular module in AutoGluon offers an insightful feature importance mechanism that helps users understand the impact of each feature on the model's predictions. This is not only crucial for model interpretation but also for improving model performance by focusing on the most influential features. Implementing a similar feature for the time-series module would provide users with a comprehensive tool for time-series forecasting that is not only powerful but also interpretable.

• Model Transparency: Provides clear insights into how and why predictions are made, increasing trust in the model.

• Feature Engineering: Identifies which features are most valuable, guiding users on where to focus their feature engineering efforts.

• Model Improvement: Helps in diagnosing model performance issues by highlighting features that are less important or potentially noisy.

Suggested Implementation:

It would be extremely helpful for the time-series module to incorporate a feature importance mechanism. This could potentially leverage some modified version of existing frameworks like SHAP (SHapley Additive exPlanations) or permutation importance, similar to the approach used in the tabular module.

The addition of feature importance explainability to the AutoGluon time-series module would be a valuable enhancement, making the module not only a powerful forecasting tool but also an interpretable and transparent one. It would align with the growing need for explainable AI in critical applications and facilitate a deeper understanding and trust in AI-driven forecasting models.

Thank you for considering this feature request. I believe it would make a significant contribution to the AutoGluon toolkit and its user community.