Multiscale Time-Series Modeling

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8 papers in the last four weeks, up 167% on the four weeks before. 0.1% of all new papers.

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SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Jun 18, 2026Feng Wu, Harsh Deep, Eric Lehman +5Electrocardiography Self-Supervised Time Series Representation Learning
Massachusetts Institute of Technology · OpenEvidence, USA · New York University +3
ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics
Jun 16, 2026Kexin Wu, Luonan Chen, Renxiao WangSpatiotemporal Forecasting Molecular Dynamics Simulation
Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China · School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
Robust Spoofed Speech Detection via Temporal Pyramid Modeling
Jun 15, 2026Mahtab Masoudi Nezhad, Nima KarimianAudio Deepfake Detection ASVspoof
Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown, WV · Bellini College of Artificial Intelligence, Cybersecurity and Computing University of South Florida Tampa, FL
MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting
Jun 11, 2026Lilan Peng, Yandi Liu, Qingren Yao +2Spatiotemporal Forecasting Multiscale Time-Series Modeling
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China · Eindhoven University of Technology, Eindhoven 5612 AZ, Netherlands
Multi-Rate Mixture of Experts for Accelerating Liquid Neural Network Training
Jun 10, 2026Shilong Zong, Almuatazbellah Boker, Hoda EldardiryIrregular Time-Series Modeling Liquid Neural Networks
Department of Computer Science, Virginia Tech Blacksburg, VA 24061 USA · Department of Electrical and Computer Engineering, Virginia Tech Blacksburg, VA 24061 USA
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
Jun 9, 2026Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers +4Multivariate Time Series Forecasting Residual Learning
Artificial Intelligence Department, RobotBulls Labs Geneva, Switzerland · Department of Electrical and Computer Engineering, North South University Dhaka, Bangladesh
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Jun 8, 2026Shuhao Li, Weidong Yang, Yue Cui +4Traffic Flow Estimation Spatiotemporal Forecasting
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200082, China · Tongyi Lab, Alibaba Group, Cloud Valley, NO.1008 Dengcai Street, Xihu District, Hangzhou 310020, China · The Hong Kong University of Science and Technology, Hong Kong SAR 999077, China +1
Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction
Jun 4, 2026Jihoon Kim, Saeyon Kim, Heejung YounRecurrent Neural Networks Spatiotemporal Forecasting
Department of Civil and Environmental Engineering, Hongik University, Seoul 04066, Republic of Korea
HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation
Jun 3, 2026Hongfan Gao, Wangmeng Shen, Bin Yang +1Time Series Imputation Multiscale Time-Series Modeling
School of Data Science and Engineering East China Normal University
FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting
May 31, 2026Peng He, Yao Liu, Yanglei Gan +3Multivariate Time Series Forecasting Frequency-Domain Feature Learning
University of Electronic Science and Technology of China · Chengdu, Sichuan, China
MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting
May 30, 2026Yu Liu, Ming Huang, Xiao Ren +3Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, China
Generalizing Multi-Scale Time-Series Modeling with a Single Operator
May 29, 2026Cheonwoo Lee, Dooho Lee, Doyun Choi +1Long-Term Time Series Forecasting Time Series Forecasting
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea · Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
May 24, 2026Da Zhang, Bingyu Li, Zhiyuan Zhao +3Graph Structure Learning Selective SSMs
Northwest Polytechnical University · TeleAI, China Telecom · TeleAI +1
PESD-TSF: A Period-Aware and Explicit Structured Decomposition Framework for Long-Term Time Series Forecasting
May 15, 2026Hua Wang, Xianhao Jiao, Fan ZhangMultivariate Time Series Forecasting Long-Term Time Series Forecasting
School of Computer and Artificial Intelligence, Ludong University, Yantai, Shandong 264025, China · School of Computer and Artificial Intelligence, Ludong University, Yantai, Shandong 264025, China. · School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong 264005, China +1
Nested Spatio-Temporal Time Series Forecasting
May 15, 2026Yinghao Ai, Yukai Zhou, Ruoxi Jiang +8Non-Stationary Time Series Forecasting Spatiotemporal Forecasting
National Key Laboratory for Novel Software Technology, Nanjing University, China · Shanghai Academy of AI for Science, Shanghai · Department of Computer Science and Technology, Nanjing University +3
Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
May 13, 2026Tomoyoshi Kimura, Denizhan Kara, Jinyang Li +6Internet of Things Multiscale Time-Series Modeling
University of Illinois Urbana-Champaign · Hong Kong University of Science and Technology · Shanghai Jiao Tong University
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling
May 11, 2026Sheng Pan, Ming Jin, Bo Du +1Neural Controlled Differential Equations Time Series Forecasting
Griffith University
ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
May 11, 2026Jinwoong Kim, Sangjin ParkIrregular Time-Series Modeling Clinical Outcome Prediction
Department of Industrial Data Engineering Hanyang University Seoul, Republic of Korea
Learning stochastic multiscale models through normalizing flows
May 10, 2026Anan Saha, Arnab GangulyStochastic Differential Equations Multiscale Time-Series Modeling
Department of Mathematics Louisiana State University Baton Rouge, LA 70802
FRACTAL: SSM with Fractional Recurrent Architecture for Computational Temporal Analysis of Long Sequences
May 9, 2026Mengqi Li, Wensheng Lin, Jinshuai Yang +1Long-Context Modeling Multiscale Time-Series Modeling
School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China. · DecoreX Intelligent Technologies Co., Ltd., Xi’an 710075 China.
NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
May 8, 2026Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-ThiemeMultivariate Time Series Forecasting Time Series Forecasting
1ISMLL, University of Hildesheim, VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany.
PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
May 8, 2026Sangyoon Bae, Shinjae Yoo, Jiook ChaDomain Generalization Physics-Informed ML
Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul, South Korea, 08826 · Department of Psychology Seoul National University Seoul, South Korea, 08826
Dual-Scale Temporal Fusion Reveals Structured Predictability in Subseasonal-to-Seasonal Temperature Prediction
May 7, 2026Elnaz Bashir, Jiali Wang, Lin YanTime Series Forecasting Multi-Scale Feature Fusion
Department of Computer Science, Iowa State University, Ames, IA 50011, USA · Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
MSMixer: Learned Multi-Scale Temporal Mixing with Complementary Linear Shortcut for Long-Term Time Series Forecasting
May 4, 2026Ahmed CherifLong-Term Time Series Forecasting Multilayer Perceptrons
Independent Researcher, Tunis, Tunisia
TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
May 2, 2026Seokhyun Lee, Jaeho Kim, Changjun Oh +2Time Series Generation Multiscale Time-Series Modeling
Department of Artificial Intelligence, Korea University, Seoul, South Korea · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
Apr 20, 2026Zhiyuan Ma, Zeyuan Li, Zihao Qiu +6Cross-Subject EEG Decoding EEG Decoding
School of Biomedical Engineering, Tsinghua Laboratory of Brain and Intelligence, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China · School of Basic Medical Sciences and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China · Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China +1
TriTS: Time Series Forecasting from a Multimodal Perspective
Apr 17, 2026Xiang AoLong-Term Time Series Forecasting Cross-Modal Representation Learning
School of Software Engineering, Beijing Jiaotong University Beijing 100044, China
Hybrid Spectro-Temporal Fusion Framework for Structural Health Monitoring
Apr 17, 2026Jongyeop Kim, Jinki Kim, Doyun LeeFrequency-Domain Feature Learning Multiscale Time-Series Modeling
aDepartment of Information Technology, Georgia Southern University Statesboro, GA, USA · bDepartment of Mechanical Engineering, Georgia Southern University Statesboro, GA, USA · cDepartment of Civil Engineering and Construction, Georgia Southern University Statesboro, GA, USA
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
Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators
Mar 12, 2026Samaksh Ujjawal, Naveen Sudheer Nair, Shivansh Pratap Singh +3Aerial Robotics Test-Time Adaptation
1Robotics Research Center, IIIT Hyderabad, India. · Department of Computer Science, University of Manchester, UK. · 3Newcastle University, UK.

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SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models

Feng Wu, Harsh Deep, Eric Lehman, Sanyam Kapoor+4

Massachusetts Institute of Technology · OpenEvidence, USA · New York University+3

Electrocardiography Self-Supervised Time Series Representation Learning Electrocardiogram Classification Multiscale Time-Series Modeling State Space Models

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Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data. While recent self-supervised learning (SSL) methods, based on various encoder architectures such as convolutional neural networks, have been proposed to learn representations from unlabeled data, they often fall short in capturing long-range dependencies and noise-invariant features. Structured state space models (S4) excel at long-sequence modeling, but existing S4 architectures fail to capture the unique characteristics of multichannel physiological waveforms. In this work, we propose SL-S4Wave, a self-supervised learning framework that combines contrastive learning with a tailored encoder built on structured state space models. The encoder incorporates multi-layer global convolution using multiscale subkernels, enabling the capture of both fine-grained local patterns and long-range temporal dependencies in noisy, high-resolution multichannel waveforms. Extensive experiments on real-world datasets demonstrate that SL-S4Wave (1) consistently outperforms state-of-the-art supervised and self-supervised baselines in a challenging arrhythmia detection task, (2) achieves high performance with significantly fewer labeled examples, showcasing strong label efficiency, and (3) maintains robust performance on long waveform segments, highlighting its capacity to model complex temporal dynamics in long sequences that most existing approaches fail to efficiently model, and (4) transfers effectively to unseen arrhythmia types, underscoring its robust cross-domain generalization. We additionally evaluate SL-S4Wave on multiple EEG tasks, achieving superior performance over strong baselines, demonstrating generalizability of our approach beyond cardiac waveforms.

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