  We propose a novel framework to classify large-scale time series data with
long duration. Long time seriesclassification (L-TSC) is a challenging problem
because the dataoften contains a large amount of irrelevant information to
theclassification target. The irrelevant period degrades the classifica-tion
performance while the relevance is unknown to the system.This paper proposes an
uncertainty-aware multiple instancelearning (MIL) framework to identify the
most relevant periodautomatically. The predictive uncertainty enables designing
anattention mechanism that forces the MIL model to learn from thepossibly
discriminant period. Moreover, the predicted uncertaintyyields a principled
estimator to identify whether a prediction istrustworthy or not. We further
incorporate another modality toaccommodate unreliable predictions by training a
separate modelbased on its availability and conduct uncertainty aware fusion
toproduce the final prediction. Systematic evaluation is conductedon the
Automatic Identification System (AIS) data, which is col-lected to identify and
track real-world vessels. Empirical resultsdemonstrate that the proposed method
can effectively detect thetypes of vessels based on the trajectory and the
uncertainty-awarefusion with other available data modality
(Synthetic-ApertureRadar or SAR imagery is used in our experiments) can
furtherimprove the detection accuracy.
