Interpretable Time Series Clustering Using Local Explanations
This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. Then, we use interpretability methods to explain the decisions of the classification models. The explanations are used to obtain insights into the clustering models. We perform a detailed numerical study to test the proposed approach on multiple datasets, clustering models, and classification models. The analysis of the results shows that the proposed approach can be used to explain time series clustering models, specifically when the underlying classification model is accurate. Lastly, we provide a detailed analysis of the results, discussing how our approach can be used in a real-life scenario.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationClusteringTime SeriesTime Series AnalysisTime Series ClusteringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with ce…
ClusteringGraph EmbeddingTime SeriesTime Series ClusteringL2GTX: From Local to Global Time Series Explanations
Deep learning models achieve high accuracy in time series classification, yet understanding their class-level decision behaviour remains challenging. Explanations for time series must respect temporal dependencies and id…
Time Series ClassificationSelf-Interpretable Time Series Prediction with Counterfactual Explanations
Interpretable time series prediction is crucial for safety-critical areas such as healthcare and autonomous driving. Most existing methods focus on interpreting predictions by assigning important scores to segments of ti…
Autonomous DrivingcounterfactualCounterfactual InferencePrediction+2TsSHAP: Robust model agnostic feature-based explainability for time series forecasting
A trustworthy machine learning model should be accurate as well as explainable. Understanding why a model makes a certain decision defines the notion of explainability. While various flavors of explainability have been w…
Time SeriesTime Series ForecastingGALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the transitions that move an instance across c…