Dynamic selection of machine learning models for time-series data

Rotem Hananya, Gilad Katz

Abstract: Highlights•A meta-learning-based approach for time-series classification model selection.•The model is trained on multiple datasets and is therefore robust and generic.•Extensive evaluation on 46 datasets demonstrates the strength of our approach.