EFFECTS: Explorable and Explainable Feature Extraction Framework for Multivariate Time-Series Classification
Ido Ikar, Amit Somech · 2023
We demonstrate EFFECTS, an automated system for explorable and explainable feature extraction for multivariate time series classification. EFFECTS has a twofold contribution: (1) It significantly facilitates the exploration of MTSC data, and (2) it generates informative yet intuitive and explainable features to be used by the classification model. EFFECTS first mines the MTS data and extracts a set of interpretable features using an optimized transform-slice-aggregate process. To evaluate the quality of EFFECTS features, we gauge how well each feature distinguishes between every two classes, and how well they characterize each single class. Users can then explore the MTS data via the EFFECTS Explorer, which facilitates the visual inspection of important features, dimensions, and time slices. Last, the user can use the top features for each class when building a classification pipeline.