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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TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations
Oct 5, 2026Johann FaouziTime Series Classification Ensemble Learning
Univ Rennes, Ensai, CNRS, CREST - UMR 9194, F-35000 Rennes, France.
The Effect of Missingness-Pattern Mismatch on Method Selection for Time-Series Classification: A Controlled Empirical Study
Oct 4, 2026Ruiqi Zhao, Zishun Yuan, Zhentao Wang +3Model Selection Time Series Classification
The University of Tokyo, Tokyo, Japan · Stony Brook University, SUNY Korea, Incheon, South Korea · Fuyao University of Science and Technology, Fujian, China +3
LogSig-SSM: Time-Series Modelling with Multi-Scale Log-Signature Compression for State-Space Models
Oct 4, 2026Felix Oury, Nicolas Calvo Peiro, Reiko J. TanakaIrregular Time-Series Modeling Multivariate Time Series Forecasting
Department of Computing Imperial College London · Department of Bioengineering Imperial College London
TopTimeNet: Topologically-assisted time-series classification model
Sep 30, 2026Sharareh Sayyad, Sophia BazziTime Series Classification Classification
Department of Mathematics and Statistics, Washington State University, Pullman, Washington 99164-3113, USA · European Molecular Biology Laboratory, EMBL Hamburg, c/o DESY, Building 25A, Notkestraße 85, 22603 Hamburg, Germany
Towards Robust Time Series Learning via Capacity-Centric Modulation
Sep 30, 2026Siru Zhong, Senzhang Wang, James T. Kwok +1Time Series Classification Neural Network Robustness
Data Science & Analytics Thrust, The Hong Kong University of Science and Technology (GZ), Guangzhou, China · School of Computer Science & Engineering, Central South University, Changsha, China · Dept. of Computer Science & Engineering, The Hong Kong University of Science and Technology, Hong Kong
A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification
Sep 30, 2026Francesco SpinnatoIrregular Time-Series Modeling Time Series Classification
Department of Computer Science, University of Pisa, Italy · ISTI-CNR, Pisa, Italy
Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective
Sep 28, 2026Guoqi Yu, Juncheng Wang, Shujun WangMultivariate Time Series Classification Time Series Classification
Department of Biomedical Engineering and Sports Technology The Hong Kong Polytechnic University
MASCIT: A Mask-Aware State Space Classifier for Naturally Irregular Time Series
Sep 28, 2026Yoo-Min Jung, Hyeon-Gi Kim, Jonghun ParkIrregular Time-Series Modeling Selective SSMs
Department of Industrial Engineering Seoul National University, Seoul, Republic of Korea
SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification
Sep 24, 2026Zhenyi Zhu, Jacqueline Pang, Peilin Shen +7Tabular Foundation Models Time Series Classification
The Chinese University of Hong Kong, Hong Kong · Cornell University, USA · Southeast University, China +7
Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting
Sep 24, 2026Xiaohan Jiang, Jingyuan Wang, Jiahao Ji +3Time Series Classification Frequency-Domain Feature Learning
School of Computer Science and Engineering, Beihang University, Beijing, China · School of Computer Science and Engineering and the School of Economics and Management, Beihang University, Beijing, China · School of Computer Science and Engineering, Beihang University, Beijing, China, and are also with the MOE Engineering Research Center of Advanced Computer Application Technology, Beihang University, China +1
Learnable Time-Frequency Masks for Explaining Time-Series Classifiers
Sep 24, 2026Theresa Dahl Frehr, Francisco Pelayo, Lukas Raad +3Feature Attribution Time Series Classification
Department of Applied Mathematics and Computer Science Technical University of Denmark
Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments
Sep 24, 2026Stefan Reitmann, Lena OdenTime Series Classification Wearable Sensing
Faculty of Computer Science, Chemnitz University of Technology, Chemnitz, Germany · Faculty of Computer Science, University of Hagen, Hagen, Germany
Evaluating the Effectiveness of SechKAN on 1D Data
Sep 22, 2026Hoang-Thang TaTime Series Classification Kolmogorov-Arnold Networks
University of Information Technology, Ho Chi Minh City, Vietnam · Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift
Sep 20, 2026Kemal Kirtac, Carsten MapleDistribution Shift Robustness Benchmark Auditing
Department of Computer Science, University College London 66–72 Gower Street, London WC1E 6EA, United Kingdom · Department of Computer Science, University of Warwick Coventry CV4 7AL, United Kingdom · WMG, University of Warwick Coventry CV4 7AL, United Kingdom
Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure
Sep 14, 2026Jiseok Lee, Brian Kenji IwanaTime Series Classification Transfer Learning
Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi Ward, Fukuoka, 819-0395, Fukuoka, Japan · Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi Ward, Fukuoka, 819-0395, Fukuoka, Japan
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
Sep 9, 2026Ian C. Guzmán, Radu Babiceanu, Berker PeközTime Series Classification Edge ML
Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA · Western Michigan University, Kalamazoo, MI 49008, USA
SMart: A Multi-source Multi-phase Time Series Representation Transfer Framework
Sep 2, 2026Fang He, Wang-chien LeeTime Series Representation Learning Transfer Learning
Department of Computer Science and Engineering, The Pennsylvania State University, PA, USA
A convolutional framework for detecting event-driven dynamics in energy price series
Aug 31, 2026Caixia Xu, Piotr FryzlewiczTime Series Classification Neural Network Approximation Theory
1Shanghai University of Finance and Economics · 2London School of Economics
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Aug 31, 2026Jérémie Stym-Popper, Clément Rambour, Federica Granese +2Tabular Foundation Models Time Series Classification
Sorbonne Université, CNRS, ISIR, MLIA, F-75005 Paris, France · Inria, CNRS, I3S, Université Côte d’Azur, Valbonne, France · INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69621, LYON, France +1
Local Reference Geometry Residual Augmentation for Imbalanced Time Series Classification
Aug 31, 2026Chuanhang Qiu, Yanran Xu, Yue Wang +1Class-Imbalanced Learning Data Augmentation
School of Electronics and Computer Science University of Southampton Southampton, United Kingdom
Supraglacial Lake Fate Is Knowable Long Before the Season Ends
Aug 31, 2026Emam Hossain, Md Osman GaniTime Series Classification Climate Science
University of Maryland, Baltimore County Baltimore, Maryland, USA
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Aug 25, 2026Juntao Fang, Shifeng Xie, Ruichu Cai +6Multivariate Time Series Classification Time Series Classification
Guangdong University of Technology · Huawei Noah’s Ark Lab · Université Paris Cité +2
Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
Aug 12, 2026Ziqiang Li, Yun Liu, Gouhei TanakaDynamic Time Warping Time Series Classification
Department of Computer Science, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan · International Research Center for Neurointelligence, The University of Tokyo, Tokyo, 113-0033, Japan. · Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto, 606-8585, Japan
FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification
Aug 8, 2026Pingping Liu, Muyao Wang, Zijian Zhang +5Multivariate Time Series Classification Time Series Classification
1Jilin University · 2Hong Kong Polytechnic University · 3Pengcheng Laboratory +1
Is Self-Pretraining really useful to improve diagnosis in medical Time Series?
Aug 6, 2026Omar Coser, Antonio Orvieto, Paolo Soda +1Time Series Classification Self-Supervised Time Series Representation Learning
Unit of Artificial Intelligence & Computer Systems, Universit`a Campus Bio-Medico di Roma, Via ´Alvaro del Portillo, 21, Rome, 00128, Italy. · Unit of Advanced Robotics and Human-Centered Technologies, Universit`a Campus Bio-Medico di Roma, Via ´Alvaro del Portillo, 21, Rome, 00128, Italy. · Max Planck Institute for Intelligent Systems +2
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
Aug 5, 2026Udo Schlegel, Julian Rakuschek, Thomas Seidl +3Counterfactual Explanations Time Series Classification
LMU Munich, Munich, Germany · TU Graz, Institute of Visual Computing, Graz, Austria · Munich Center for Machine Learning (MCML), Munich, Germany +3
Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series
Aug 5, 2026Thorsten Hoeser, Felix Bachofer, Claudia KuenzerTime Series Classification Recurrent Neural Networks
Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany · Institute for Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany
TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model
Aug 4, 2026Gabriel da Costa Merlin, Diego Furtado SilvaTime Series Classification
University of São Paulo (USP), São Carlos, SP, Brazil
Statistical comparisons of time-series feature sets on classification tasks
Aug 3, 2026Trent Henderson, Ben D. FulcherTime Series Classification Classification
Dynamics and Neural Systems Lab, The University of Sydney, Camperdown, 2006, NSW, Australia.
CENDRe: Concept Extraction with Natural Domain Representations
Jul 31, 2026Antonia Holzapfel, Andres Felipe Posada Moreno, Sebastian TrimpeTime Series Classification Convolutional Neural Networks
Institute for Data Science in Mechanical Engineering (DSME) RWTH Aachen University Theaterstraße 35-39, 52062 Aachen, Germany

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TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

Johann Faouzi

Univ Rennes, Ensai, CNRS, CREST - UMR 9194, F-35000 Rennes, France.

Time Series Classification Ensemble Learning

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A representation family is a distinct way of extracting features from time series. Ensemble algorithms that combine several representation families remain the most accurate approach to time series classification. Current state-of-the-art ensembles, most notably HIVE-COTE2.0, pair a bespoke classification algorithm with each representation family and combine their predictions using a fixed, non-adaptive rule. We present TIGER (Time-series classification with In-context-learning Gated Ensemble of Representations), which instead applies the same small portfolio of three general-purpose classifiers (Ridge, Extra Trees, and Naive Bayes) to four representations from four distinct families, stacking the resulting twelve base learners' predictions into a meta-feature matrix. The final prediction is produced by an adaptive meta-classification rule that chooses, independently for each data set, between a weighted hard majority vote and TabICLv2, a pretrained tabular foundation model used in-context as a meta-classifier, based on the mean number of training samples available per class. On a 142-data-set benchmark drawn from the UCR time series classification archive, TIGER obtains the best mean accuracy, balanced accuracy, and F1-score among six compared algorithms, including HIVE-COTE2.0, and significantly outperforms each of the other five individually. TIGER's adaptive rule also meaningfully outperforms either of its two constituent meta-classification methods used alone, and its single hyperparameter, tuned using only a twenty-data-set development subset, is shown to generalize to the full evaluation benchmark. We further characterize TIGER's design through an extensive set of ablation experiments and report the design alternatives that we investigated and ultimately discarded.

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