Blending Ensemble applied to Open-Set Recognition for Time Series Classification

Time Series (TS), including stock prices, temperatures, and health markers are monitored all the time and everywhere. Time-dependent events, and TS are frequently studied in the scope of forecasting, where past values of the TS are used to preview future ones. On the other hand, Time Series Classification (TSC) aims at creating models that label TS instances. Open Set Recognition (OSR) techniques, designed to classify known samples and detect unknowns simultaneously, have been less applied in TSC compared to other fields like image classification. Existing methods have limitations, such as not using known-unknown in the training stage, limited transfer learning across Neural Network models, and experiments with benchmark datasets with narrow coverage. This study introduces a novel OSR approach for TSC by addressing the mentioned issues and using a blending ensemble of Neural Networks with an OpenMax layer. The results vouch for the model’s performance and potential superiority as an alternative to existing methods in open-set recognition tasks.