Time Series Forecasting

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Time series forecasting studies how to predict future observations from past values, often across multiple horizons and with missing data, changing regimes, or external drivers such as weather and events. Methods range from classical autoregressive and exponential-smoothing models to recurrent networks, Transformers, state-space models, decomposition and frequency-based approaches, graph models, and pretrained foundation models; probabilistic methods also estimate uncertainty. Current work increasingly combines historical patterns with covariates, retrieval, domain knowledge, and online adaptation, while targeting applications from energy and weather to health, transport, and finance. A central tension is that larger or more complex models do not reliably beat simple baselines, and benchmark accuracy may not reflect robustness, forecast structure, uncertainty calibration, or downstream decision value.

Latest papers

876 papers143 papers in the last four weeks, up 32% on the four weeks before. 0.8% of all new papers.

ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Feb 26, 2026Haohui Jia, Zheng Chen, Lingwei Zhu +6Latent Dynamics Modeling Electroencephalography
Information Science and Techinology, Hokkaido University, Japan · SANKEN, The University of Osaka, Japan · Great Bay University, China +1
Switch-Hurdle: A MoE Encoder with AR Hurdle Decoder for Intermittent Demand Forecasting
Feb 26, 2026Fabian Muşat, Simona CăbuzTime Series Forecasting Retail Demand Forecasting
eMAG, Romania
Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support
Feb 26, 2026Md Tanvir Hasan TurjaTime Series Forecasting Explainable Time Series Forecasting
Independent Researcher, London, United Kingdom
TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
Feb 26, 2026Xiannan Huang, Shen Fang, Shuhan Qiu +3Time Series Forecasting Residual Dynamics Learning
Key Laboratory of Road and Traffic Engineering, Ministry of Education Tongji University, Shanghai China · Research Center for Scientific Data Hub, Zhejiang Lab, Hangzhou, China · The College of Computer Science, Tongji University, Shanghai China
DECODE: Dual-Enhanced Conditioned Diffusion for EEG Forecasting
Feb 25, 2026Mehran Shabanpour, Sadaf Khademi, Konstantinos N Plataniotis +1EEG Signal Generation Time Series Forecasting
Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada. · Department of Electrical and Computer Engineering, University of Toronto, Canada
DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
Feb 25, 2026Jinpeng Li, Zhongyi Pei, Huaze Xue +3Multivariate Time Series Forecasting Time Series Forecasting
School of Software, BNRist, Tsinghua University · Tencent
Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Feb 25, 2026Xiannan Huang, Quan Yuan, Chao YangTraffic Forecasting Time Series Forecasting
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University, 4800 Cao’an Road, Shanghai, 201804, Shanghai, China · Urban Mobility Institute, Tongji University, 1239 Siping Road, Shanghai, 200092, Shanghai, China
Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
Feb 25, 2026Sijie Ruan, Jinyu Li, Jia Wei +7Epidemic Forecasting Spatiotemporal Forecasting
Beijing Institute of Technology, China · Beijing Institute of Technology, Beijing, China · Zhejiang Center for Disease Control and Prevention, China +6
F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performance Optimization
Feb 24, 2026Xuran Ma, Xuebao Li, Yanfang Zheng +15Time Series Forecasting Wavelet Methods
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, People′s Republic of China · State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, People′s Republic of China · Yunnan Astronomical Observatory, Chinese Academy of Sciences, Kunming 650216, China +3
From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
Feb 21, 2026Suhasnadh Reddy Veluru, Sai Teja Erukude, Viswa Chaitanya MarellaElectricity Load Forecasting Time Series Forecasting
Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India · Department of Electronics and Communication Engineering, Bharat Institute of Engineering and Technology, Hyderabad, Telangana, India
Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment
Feb 20, 2026Baris Arat, Hasan Fehmi Ates, Emre SeferTime Series Forecasting Long-Term Time Series Forecasting
Ozyegin University, Istanbul, Turkey
PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
Feb 20, 2026Shubham Bhardwaj, Chandrajit BajajTime Series Forecasting Physics-Informed Forecasting
Department of Computer Science, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
Financial time series augmentation using transformer based GAN architecture
Feb 19, 2026Andrzej Podobiński, Jarosław A. ChudziakTime Series Data Augmentation Financial Time Series
Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland
AdaptStress: Online Adaptive Learning for Interpretable and Personalized Stress Prediction Using Multivariate and Sparse Physiological Signals
Feb 19, 2026Xueyi Wang, Claudine J. C. Lamoth, Elisabeth WilhelmWearable Health Monitoring Explainable Time Series Forecasting
Engineering and Technology institute Groningen (ENTEG), University of Groningen, Groningen, 9742AG, the Netherlands · Department of Human Movement Sciences, University Medical Center Groningen, Groningen, Netherlands
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Feb 19, 2026Xihao Piao, Zheng Chen, Lingwei Zhu +3Frequency-Domain Time Series Modeling Non-Stationary Time Series Forecasting
SANKEN Osaka University Osaka, Japan · School of Information Science and Technology Great Bay University Dongguan, China · Department of Computer Science Florida State University
HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing
Feb 19, 2026Srikumar NayakPortfolio Optimization Time Series Forecasting
Sr. Member IEEE, Incedo Inc, USA.
A statistical perspective on transformers for small longitudinal cohort data
Feb 18, 2026Kiana Farhadyar, Maren Hackenberg, Kira Ahrens +10Permutation Testing Time Series Transformers
Institute of Medical Biometry and Statistics (IMBI), University of Freiburg, Freiburg, Germany · Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany · Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany +6
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Feb 18, 2026Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2Time Series Modeling Time Series Forecasting
Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany · Faculty of Physics & Astronomy, Heidelberg Univ., Heidelberg, Germany · Interdisciplinary Center for Scientific Computing, Heidelberg, Germany
AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
Feb 18, 2026Maria Luisa Taccari, Kenza Tazi, Oisín M. Morrison +7Streamflow Forecasting Time Series Forecasting
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom · European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
Feb 18, 2026Xu Zhang, Peng Wang, Yichen Li +1Instance Reweighting Time Series Forecasting
Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · Department of Electrical and Computer Engineering, University of British Columbia (UBC), Vancouver, Canada
R $^2$ Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Feb 17, 2026Zhi Sheng, Yuan Yuan, Guozhen Zhang +1Forecasting Benchmarks Time Series Forecasting
Center for Urban Science and Computation, Tsinghua University · Courant Institute of Mathematical Sciences, New York University
Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
Feb 17, 2026Kevin Valencia, Xihaier Luo, Shinjae Yoo +1Spatiotemporal Forecasting Diffusion-Based Inverse Problems
University of California, Los Angeles · Brookhaven National Laboratory
Rethinking the Role of LLMs in Time Series Forecasting
Feb 16, 2026Xin Qiu, Junlong Tong, Yirong Sun +3LLM-Based Time Series Forecasting Time Series Forecasting
Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Zhejiang University · LMU Munich
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
Feb 16, 2026Tobia Boschi, Andrea Loreti, Nicola C. Amorisco +13Multimodal Foundation Models Multimodal Pretraining
IBM Research Dublin, Ireland · UK Atomic Energy Authority Abingdon, UK · STFC Hartree Centre Daresbury, UK
FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting
Feb 16, 2026Qingzhong Li, Yue Hu, Zhou Long +3Time Series Forecasting Time Series Transformers
School of Software, Xinjiang University, Urumqi, China · Xinjiang Key Laboratory of Intelligent Computing and Smart Applications, School of Software, Xinjiang University, Urumqi 830046, China
Adaptive Automatic Model Selection for Demand Forecasting under Heterogeneous Demand Patterns
Feb 15, 2026Adolfo González, Víctor ParadaTime Series Forecasting Forecasting Benchmarks
Department of Computer Engineering and Informatics, Faculty of Engineering, University of Santiago of Chile, Santiago, Chile
Learning Agentic Time Series Forecasting via Trajectory Distillation and Memory-Based Reinforcement Learning
Feb 14, 2026Xiaoyu Tao, Mingyue Cheng, Yaguo Liu +4Agentic Time Series Forecasting Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Feb 13, 2026Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2Gradient-Boosted Decision Trees Ensemble Learning
Vrije Universiteit Brussel, Brussels, Belgium · Norwegian University of Science and Technology, Trondheim, Norway
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
Feb 6, 2026Haoran Zhang, Haixuan Liu, Yong Liu +4Exogenous Time Series Forecasting Time Series Forecasting
School of Software, BNRist, Tsinghua University
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
Feb 4, 2026Ziyu Zhou, Yuchen Fang, Weilin Ruan +4Climate Science Time Series Forecasting
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · DAMO Academy, Alibaba Group, Hangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China

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ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks

Haohui Jia, Zheng Chen, Lingwei Zhu, Rikuto Kotoge+5

Information Science and Techinology, Hokkaido University, Japan · SANKEN, The University of Osaka, Japan · Great Bay University, China+1

Latent Dynamics Modeling Electroencephalography Time Series Forecasting Brain Network Analysis

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Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model continuous brain dynamics through discretizing time with recurrent architecture, which necessarily results in compounded cumulative prediction errors and failure of capturing instantaneous, nonlinear characteristics of EEGs. We propose ODEBRAIN, a Neural ODE latent dynamic forecasting framework to overcome these challenges by integrating spatio-temporal-frequency features into spectral graph nodes, followed by a Neural ODE modeling the continuous latent dynamics. Our design ensures that latent representations can capture stochastic variations of complex brain states at any given time point. Extensive experiments verify that ODEBRAIN can improve significantly over existing methods in forecasting EEG dynamics with enhanced robustness and generalization capabilities.