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We propose a novel framework to classify large-scale time series data withlong duration. Long time seriesclassification (L-TSC) is a challenging problembecause the dataoften contains a large amount of irrelevant information totheclassification target. The irrelevant period degrades the classifica-tionperformance while the relevance is unknown to the system.This paper proposes anuncertainty-aware multiple instancelearning (MIL) framework to identify themost relevant periodautomatically. The predictive uncertainty enables designinganattention mechanism that forces the MIL model to learn from thepossiblydiscriminant period. Moreover, the predicted uncertaintyyields a principledestimator to identify whether a prediction istrustworthy or not. We furtherincorporate another modality toaccommodate unreliable predictions by training aseparate modelbased on its availability and conduct uncertainty aware fusiontoproduce the final prediction. Systematic evaluation is conductedon theAutomatic Identification System (AIS) data, which is col-lected to identify andtrack real-world vessels. Empirical resultsdemonstrate that the proposed methodcan effectively detect thetypes of vessels based on the trajectory and theuncertainty-awarefusion with other available data modality(Synthetic-ApertureRadar or SAR imagery is used in our experiments) canfurtherimprove the detection accuracy.

Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data