Counterfactual explanations for time series

Sammanfattning: Time Series are used in healthcare, meteorology, and many other fields. Rigorous research has been done to develop distance measures and classifying algorithms for time series. When a time series is classified, one can ask what changes should be made to the time series to classify it differently. A time series with the appropriate changes that make the classifier classify the time series as a different class is known as a counterfactual explanation. There exist model-dependent methods for creating counterfactual explanations. However, there exists a lack in the literature of a model agnostic method for creating counterfactual explanations for Time Series. This study aims to answer the following research question. ” How does a model agnostic method for counterfactuals for time series perform in terms of cost and compactness compared to model dependent algorithms for counterfactuals for time series?” To answer the research question, a model agnostic method for creating counterfactuals for time series was created named Multi-Objective Counterfactuals For Time Series. The Evaluation of the Multi-Objective Counterfactual Explanation For Time Series performed better than the modeldependent algorithms in Compactness but worse in Cost.