Time Series Classification

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
Aug 24, 2025YongKyung Oh, Dong-Young Lim, Sungil Kim +1Time Series Classification Neural ODEs
University of California, Los Angeles, Los Angeles, CA, USA · Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
Learning Interpretable Differentiable Logic Networks for Time-Series Classification
Aug 24, 2025Chang Yue, Niraj K. JhaTime Series Classification Interpretable ML
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution
Aug 6, 2025Zahra Mohammadi, Parnian Fazel, Siamak MohammadiSleep Stage Classification Energy-Efficient ML
School of Electrical and Computer Engineering, University of Tehran.
Multi-Class Human/Object Detection on Robot Manipulators using Proprioceptive Sensing
Aug 4, 2025Justin Hehli, Marco Heiniger, Maryam Rezayati +1Time Series Classification Human-Robot Collaboration
Dep. of Informatics, University of Zurich, and MINDLab, Institute of Mechatronic Systems, ZHAW, Switzerland · Institute of Mechatronics system, ZHAW, Switzerland
$T^{3}S$ : Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Jun 15, 2025Mehmet Ozgur Turkoglu, Selene Ledain, Jeffrey Zweidler +2Agricultural Image Analysis Agricultural Remote Sensing
Earth Observation of Agroecosystems Team, Agroscope, Switzerland · ETH Zurich, Switzerland
Unlocking Pretrained Vision Transformers for Time Series Classification
Jun 10, 2025Simon Roschmann, Quentin Bouniot, Vasilii Feofanov +2Time Series Classification Vision Transformer
Helmholtz Munich · Technical University of Munich · Munich Center for Machine Learning +2
Mantis: Lightweight Foundation Model for Time Series Classification
Feb 21, 2025Vasilii Feofanov, Songkang Wen, Shifeng Xie +10Time Series Classification Self-Supervised Time Series Representation Learning
1. Huawei Noah’s Ark Lab · 3. Paris Cit´e University · 4. Helmholtz Munich +3
Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields
Nov 29, 2023Francesco Spinnato, Riccardo Guidotti, Anna Monreale +1Time Series Classification Interpretable ML
Scuola Normale Superiore, Pisa, Italy · University of Pisa, Pisa, Italy · ISTI-CNR, Pisa, Italy
BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification
Date pendingJingtao Zhang, Yi Liu, Qi Shen +1Efficient Neural Network Inference Transformer
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University · College of Medicine and Biological Information Engineering, Northeastern University

Aug 24, 2025, cs.LGJ/K move · Enter open · S save

TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification

YongKyung Oh, Dong-Young Lim, Sungil Kim, Alex Bui

University of California, Los Angeles, Los Angeles, CA, USA · Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

Time Series Classification Neural ODEs

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Handling missing data in time series classification remains a significant challenge in various domains. Traditional methods often rely on imputation, which may introduce bias or fail to capture the underlying temporal dynamics. In this paper, we propose TANDEM (Temporal Attention-guided Neural Differential Equations for Missingness), an attention-guided neural differential equation framework that effectively classifies time series data with missing values. Our approach integrates raw observation, interpolated control path, and continuous latent dynamics through a novel attention mechanism, allowing the model to focus on the most informative aspects of the data. We evaluate TANDEM on 30 benchmark datasets and a real-world medical dataset, demonstrating its superiority over existing state-of-the-art methods. Our framework not only improves classification accuracy but also provides insights into the handling of missing data, making it a valuable tool in practice.

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