Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification
Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model. This is known as class-incremental continual learning, and in the case of multivariate time-series, is further complicated by the temporal structure of the data. In this paper, we present a novel approach for performing class incremental continual learning for the classification of multivariate time series data based upon the construction of a dual-stream feature extraction pipeline (using both deep temporal embedding features generated via a pre-trained frozen foundation model and application of statistical...
| Authors | Pablo García-Santaclara, Bruno Fernández-Castro, Rebeca Pilar Díaz-Redondo |
|---|---|
| Primary category | stat.ML |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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