ShiFT uses deterministic temporal shifts to enforce shift invariance in contrastive learning, achieving state-of-the-art time series classification on six benchmarks plus UCR/UEA archives while cutting training time.
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A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
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Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning
ShiFT uses deterministic temporal shifts to enforce shift invariance in contrastive learning, achieving state-of-the-art time series classification on six benchmarks plus UCR/UEA archives while cutting training time.
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WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition
A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.