Dual Confidence Learning Network for Open-World Time Series Classification

Traditional time series classification methods hold a basic closed-world assumption that the classes of test examples must have been seen by the classifier. However, in the real world, new examples that do not belong to any training class are constantly generated. The existing open-world classification methods cannot be well applied to time series data because they mainly solve problems in the field of computer vision without mining temporal features. In addition, these methods are mostly based on overconfident prediction results, and an overconfident classifier may produce predictive probabilities with high confidence even for incorrect predictions. In this paper, we propose a novel dual confidence learning network for open-world time series classification, which leverages temporal deep neural model to capture temporal features, and designs a dual confidence mechanism to identify which known class or unknown class an example belongs to. Specifically, temporal confidence is learned from the likelihood sequence to reflect the true correctness risk and Weibull distribution confidence is learned to reflect the open space risk. Experimental results evaluated on real-world datasets demonstrate that the proposed model can accurately identify examples of unknown classes without sacrificing the classification performance of known class examples.