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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-219629 |
| Date | January 2022 |
| Creators | Schultz, Markus |
| Publisher | Stockholms universitet, Institutionen för data- och systemvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0016 seconds