time series classification

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papersTODAY 04:00 UTC

Shapelet-Based Distance Measure Aims to Improve Multi-Source Transfer Learning for Time Series

A new arXiv preprint proposes selecting source datasets for time series classification by measuring similarity through shapelets, the discriminative subsequences that characterize time series patterns. The authors argue that transfer learning helps overcome limited labeled data in deep learning, but its usefulness hinges on picking appropriate source datasets. Their method is presented as an alternative to conventional transferability estimation, which the paper describes as computationally expensive.

transfer-learning/time-series/arxiv/research

papersSEP 11 04:00 UTC

BiHDTrans: Binary Hyperdimensional Transformer for Edge Time Series Classification

Researchers propose BiHDTrans, a transformer variant that uses binary hyperdimensional computing to classify multivariate time series from IoT sensors. The design targets resource-constrained edge devices, where large data volumes and limited compute make standard models impractical. It is presented as an arXiv preprint focused on balancing efficiency with classification accuracy.

research/edge-computing/time-series/efficiency