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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Beyond Similarity: Temporal Operator Attention for Time Series Analysis
May 11, 2026Jevon Twitty, Vinh Pham, Nitiwith Rotchanarak +4Time Series Classification Time Series Forecasting
Georgia Institute of Technology1
One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
May 11, 2026Mengzhou Gao, Kaiwei Wang, Pengfei JiaoIrregular Time-Series Modeling Graph Structure Learning
School of Cyberspace, Hangzhou Dianzi University.
TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma
May 10, 2026JC Wu, Norton Lee, Kai Siang ChenTime Series Classification Cross-Domain Transfer Learning
TaiScience Research Group, affiliated with Fu Jen Catholic University, Taiwan · Center for Geometry and Physics, Institute for Basic Science (IBS), South Korea · Fu Jen Catholic University, Taiwan
TIDES: Implicit Time-Awareness in Selective State Space Models
May 10, 2026Taylan Soydan, Miguel A. Bessa, Dirk Mohr +1Irregular Time-Series Modeling Selective SSMs
AIMM, ETH Zürich Zurich, Switzerland
Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia
May 10, 2026Md Nasiat Hasan Fahim, Md. Abid Ullah Muhib, Rayhanul Amin Tanvir +1Time Series Classification Seismology
Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet, Bangladesh
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
May 10, 2026Lin Li, Jiawei Huang, Qihao Quan +7Multivariate Time Series Classification Time Series Classification
Sun Yat-sen University Zhuhai, Guangdong, China · Xiaomi Corporation Beijing, China · University of Science and Technology of China Hefei, Anhui, China +1
STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
May 7, 2026Unaza Tallal, Shruti Kshirsagar, Ankita ShuklaElectroencephalography Sleep Stage Classification
School of Computing, Wichita State University, 1845 N. Fairmount, Wichita, KS 67260, USA · Computer Science and Engineering Department, University of Nevada, Reno, NV, USA
Transformer-Based Wildlife Species Classification from Daily Movement Trajectories
May 7, 2026Obed Irakoze, Prasenjit MitraTime Series Classification Wildlife Monitoring
Department of Electrical & Computer Engineering Carnegie Mellon University Africa Kigali, Rwanda
A Novel Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series
May 7, 2026Motasem Alkayid, Zhengsen Xu, Saeid Taleghanidoozdoozan +7Remote Sensing Image Understanding Mamba
Department of Geomatics Engineering, University of Calgary, Canada
A Simple State Space Model Excels at Multivariate Time Series Classification
May 7, 2026Hassan Saadatmand, Geoffrey I. Webb, Hamid Rezatofighi +1Selective SSMs Multivariate Time Series Classification
Monash University, Melbourne, Victoria, Australia
Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
May 4, 2026S M Asif Hossain, Shruti KshirsagarSleep Stage Classification Fine-Tuning
School of Computing, Wichita State University, Wichita, Kansas, USA
Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification
Apr 30, 2026Erkka Rantahalvari, Olli Silvén, Zinelabidine Boulkenafet +1Biometric Identification Time Series Classification
Candour Oy, Oulu, Finland · University of Oulu, Oulu, Finland
Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series
Apr 30, 2026Christopher Holder, Anthony BagnallDynamic Time Warping Time Series Classification
School of Electronics and Computer Science University of Southampton Southampton, SO17 1BJ, United Kingdom
VTBench: A Multimodal Framework for Time-Series Classification with Chart-Based Representations
Apr 29, 2026Madhumitha Venkatesan, Xuyang Chen, Dongyu LiuData Visualization Time Series Classification
University of California, Davis Davis, United States
At the Edge of the Heart: ULP FPGA-Based CNN for On-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts
Apr 28, 2026Kazi Mohammad Abidur Rahman, Davis Rakhshan, Philipp Lütke +2Time Series Classification Wearable Health Monitoring
Smart Sensors Group, Hamburg University of Technology, Hamburg, Germany · Distributed Systems, Kiel University, Kiel, Germany · Networked Cyber-Physical Systems, Hamburg University of Technology, Hamburg, Germany
EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
Apr 28, 2026Xuanhao Yang, Bing Xue, Mengjie ZhangTime Series Classification Genetic Programming
Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand
Impact of Age Specialized Models for Hypoglycemia Classification
Apr 26, 2026Beyza Cinar, Maria MaleshkovaType 1 Diabetes Time Series Classification
Data Engineering · Helmut Schmidt University · Germany
Deep Clustering for Climate: Analyzing Teleconnections through Learned Categorical States
Apr 24, 2026Lívia Meinhardt, Dário OliveiraRepresentation Learning Time Series Classification
FGV EMAp
Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection
Apr 23, 2026Marius Huber, David R. Reich, Lena A. JägerTime Series Classification Eye Tracking
Department of Computational Linguistics, University of Zürich, Switzerland
Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series
Apr 22, 2026Thorsten Hoeser, Felix Bachofer, Claudia KuenzerTime Series Classification Remote Sensing
Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany · Institute for Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany
Scalable Memristive-Friendly Reservoir Computing for Time Series Classification
Apr 21, 2026Coşku Can Horuz, Andrea Ceni, Claudio Gallicchio +1Dynamic Neural Networks Energy-Efficient ML
Adaptive AI Research Group University of Lübeck, Germany · Department of Computer Science University of Pisa, Italy
Fully Analog Resonant Recurrent Neural Network via Metacircuit
Apr 19, 2026Zixin Zhou, Tianxi Jiang, Menglong Yang +3Efficient Neural Network Inference Time Series Classification
Institute of Humanoid Robots, CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, People’s Republic of China · State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
Univariate Channel Fusion for Multivariate Time Series Classification
Apr 17, 2026Fernando Moro, Vinicius M. A. SouzaMultivariate Time Series Classification Time Series Classification
Pontifícia Universidade Católica do Paraná (PUCPR), Brazil · Graduate Program in Informatics (PPGIa), Brazil
MedMamba: Recasting Mamba for Medical Time Series Classification
Apr 17, 2026ZhengXiao He, Huayu Li, Xiwen Chen +4Mamba Electroencephalography
Department of Electrical & Computer Engineering at the University of Arizona, Tucson, AZ, USA
ExoNet: Calibrated Multimodal Deep Learning for TESS Exoplanet Candidate Vetting using Phase-Folded Light Curves, Stellar Parameters, and Multi-Head Attention
Apr 16, 2026Md. Rashadul IslamTime Series Classification AI-Assisted Scientific Research
Dept. of Computer Science and Engineering Daffodil International University Dhaka, Bangladesh
MambaSL: Exploring Single-Layer Mamba for Time Series Classification
Apr 16, 2026Yoo-Min Jung, Leekyung KimMamba Selective SSMs
Department of Industrial Engineering, Seoul National University
Extending SSMs with the Exponentially Weighted Signature
Mar 19, 2026Alexandre Bloch, Benjamin Walker, Joël Mouterde +3Dynamical Systems Time Series Classification
Mathematical Institute, University of Oxford · SKF · Department of Mathematics, Imperial College London
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
Jan 31, 2026Juntao Fang, Shifeng Xie, Shengbin Nie +7Time Series Classification In-Context Learning
Guangdong University of Technology, Guangzhou, China · Huawei Noah’s Ark Lab, Paris, France · Paris Descartes University, Paris, France +2
Minimum distance classification for nonlinear dynamical systems
Jan 7, 2026Dominique MartinezDynamical Systems Time Series Classification
Aix-Marseille University, CNRS, ISM, 13009 Marseille, France
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
Nov 13, 2025YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series Modeling Time Series Classification
Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA), CA, USA · Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea · Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea

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Beyond Similarity: Temporal Operator Attention for Time Series Analysis

Jevon Twitty, Vinh Pham, Nitiwith Rotchanarak, Viresh Pati+3

Georgia Institute of Technology1

Time Series Classification Time Series Forecasting Temporal Attention

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A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue that this gap arises from a mismatch in the sequence-modeling primitive: while many time-series dynamics are governed by global temporal operators (e.g., filtering and harmonic structure), standard attention forms each output as a convex combination of inputs. This restricts its ability to represent signed and oscillatory transformations that are fundamental to temporal signal processing. We formalize this limitation as a simplex-constrained mixing bottleneck in softmax attention, which becomes especially restrictive for operator-driven time-series tasks. To address this, we propose

\textbf{Temporal Operator Attention (TOA)}

, a framework that augments attention with explicit, learnable sequence-space operators, enabling direct signed mixing across time while preserving input-dependent adaptivity. To make dense

N \times N

operators practical, we introduce Stochastic Operator Regularization, a high-variance dropout mechanism that stabilizes training and prevents trivial memorization. Across forecasting, anomaly detection, and classification benchmarks, TOA consistently improves performance when integrated into standard backbones such as PatchTST and iTransformer, with particularly strong gains in reconstruction-heavy tasks. These results suggest that explicit operator learning is a key ingredient for effective time-series modeling.

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