PDFTime reformulates multivariate time series classification as a multi-stage prototype-based decision process, claiming SOTA results on UCR and UEA benchmarks.
Time-series representation learning via temporal and contextual contrasting.arXiv preprint arXiv:2106.14112
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
A self-supervised method learns a fixed set of disentangled fingerprint tokens from medical time series by combining reconstruction loss with a total coding rate diversity penalty, framed as a disentangled rate-distortion problem.
CASE-NET combines a causal temporal encoder with adaptive channel recalibration and reports new state-of-the-art accuracy on four of six evaluated multivariate time series tasks.
citing papers explorer
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Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
PDFTime reformulates multivariate time series classification as a multi-stage prototype-based decision process, claiming SOTA results on UCR and UEA benchmarks.
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Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning
Proposes TA2CL framework that uses temporal asynchronous alignment in contrastive learning to improve cross-subject EEG emotion classification, reporting 64.5% accuracy on 9-class FACED, 79.5% binary on FACED, 86.4% on SEED and 70.1% on SEED-V.
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Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
A self-supervised method learns a fixed set of disentangled fingerprint tokens from medical time series by combining reconstruction loss with a total coding rate diversity penalty, framed as a disentangled rate-distortion problem.
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CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification
CASE-NET combines a causal temporal encoder with adaptive channel recalibration and reports new state-of-the-art accuracy on four of six evaluated multivariate time series tasks.