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.

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

Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
Feb 2, 2026Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1Multivariate Time Series Forecasting Frequency-Domain Time Series Modeling
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
Deep Time-series Forecasting Needs Kernelized Moment Balancing
Jan 31, 2026Licheng Pan, Haocheng Yang, Shuting He +7Time Series Forecasting Distribution Matching
Xiaohongshu Inc. · National University of Singapore · College of Computer Science and Technology, Zhejiang University
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
Jan 29, 2026Lige Zhang, Ali Maatouk, Jialin Chen +3Multivariate Time Series Forecasting Time Series Forecasting
Emory University · Yale University · Amazon
Parametric and Generative Forecasts of EPEX Day\char45 Ahead Energy Market Curves
Jan 28, 2026Julian Gutierrez, Redouane SilventeTime Series Forecasting Probabilistic Forecasting
New York University Abu Dhabi, Saadiyat Island, PO Box 129188, Abu Dhabi, United Arab Emirates · CREST, ENSAE, Institut Polytechnique de Paris, 5 Avenue Henry Le Chatelier 91120 Palaiseau, France
Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
Jan 23, 2026Haonan Yang, Jianchao Tang, Zhuo LiDisentangled Representation Learning Prototype Learning
National University of Defense Technology, Changsha, Hunan, China
GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting
Jan 8, 2026Shovito Barua Soumma, Hassan GhasemzadehTime Series Forecasting Blood Glucose Forecasting
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
Jan 4, 2026Pengfei Qu, Wenyu Ouyang, Chi Zhang +6Streamflow Forecasting Time Series Forecasting
School of Computer Science and Technology, Dalian University of Technology, Dalian, China · School of Infrastructure Engineering, Dalian University of Technology, Dalian, China · School of Information and Communication Engineering, Dalian University of Technology, Dalian, China +2
Time Series Foundation Models for Process Model Forecasting
Dec 8, 2025Yongbo Yu, Jari Peeperkorn, Johannes De Smedt +1Zero-Shot Time Series Forecasting Process Mining
Research Center for Information Systems Engineering (LIRIS), KU Leuven, Belgium
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
Dec 8, 2025Joel Ekstrand, Tor Mattsson, Zahra Taghiyarrenani +3Multivariate Time Series Forecasting Contrastive Learning
Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden · Mikael Linden Consulting AB, Stockholm, Sweden
Auditable Context-Aware HFMD Forecasting with Structured LLM Agents
Nov 28, 2025Joongwon Chae, Runming Wang, Chen Xiong +5Epidemic Forecasting Explainable Time Series Forecasting
Institute of Biomedicine and Health Engineering, Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China · The First Hospital of Hebei Medical University, Shijiazhuang, China · The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China +1
KAN vs LSTM Performance in Time Series Forecasting
Nov 23, 2025Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1Time Series Forecasting Non-Stationary Time Series Forecasting
Department of Mathematics, Swinburne University of Technology, Hawthorn, 3122, Victoria, Australia.
LOBERT: Generative AI Foundation Model for Limit Order Book Messages
Nov 16, 2025Eljas Linna, Kestutis Baltakys, Alexandros Iosifidis +1Limit Order Book Forecasting Quantitative Finance
Data Science Research Centre Tampere University
AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Time Series Forecasting
Nov 12, 2025Xiaohan Zhang, Tian Gao, Mingyue Cheng +5Time Series Forecasting LLM-Based Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China.
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
Oct 28, 2025Hao Wang, Licheng Pan, Yuan Lu +7Time Series Forecasting Optimal Transport
Xiaohongshu Inc. · College of Control Science and Technology, Zhejiang University · College of Computer Science and Technology, Zhejiang University +4
Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
Oct 28, 2025Hao Wang, Licheng Pan, Yuan Lu +7Time Series Forecasting Adaptive Loss Weighting
Xiaohongshu Inc. · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · College of Engineering, Purdue University +6
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Oct 1, 2025Nouha Karaouli, Denis Coquenet, Elisa Fromont +2Time Series Forecasting Time-Series Foundation Models
Univ. Rennes, CNRS, Inria, IRISA - UMR 6074, F-35000 Rennes, France · Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France · Univ. Grenoble Alpes, CEA, LIST, 38000 Grenoble, France
fev-bench: A Realistic Benchmark for Time Series Forecasting
Sep 30, 2025Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen +5Benchmark Design Time Series Forecasting
AWS · Keystone AI · Prior Labs +2
Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
Sep 30, 2025Kun Feng, Shaocheng Lan, Yuchen Fang +6Rotary Positional Embeddings Zero-Shot Time Series Forecasting
School of Information Science and Technology, ShanghaiTech University, Shanghai, China · Ant Group, Shanghai, China
GateTS: Versatile and Efficient Forecasting via Attention-Inspired routed Mixture-of-Experts
Aug 24, 2025Kyrylo Yemets, Mykola Lukashchuk, Ivan IzoninTime Series Forecasting Mixture-of-Experts Models
Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, 79005, Ukraine · Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AE, Netherlands · The Bartlett School of Sustainable Construction, University College London, London, WC1E 7HB, United Kingdom
DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
Aug 3, 2025Haonan Yang, Jianchao Tang, Zhuo Li +1Time Series Forecasting Multi-Scale Feature Fusion
National University of Defense Technology, Changsha, Hunan, China
Learning Intrinsic Water-Quality Dynamics with Rainfall for Data-Driven Forecasting
Aug 1, 2025Ziqi Wang, Hailiang Zhao, Cheng Bao +3Multivariate Time Series Forecasting Time Series Forecasting
Zhejiang University
Tourist arrival forecasting with game-theoretic social influence weighting and deep learning
Jul 4, 2025Ali NiksereshtTime Series Forecasting Non-Stationary Time Series Forecasting
School of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, United States
Temporal horizons in forecasting: a performance-learnability trade-off
Jun 4, 2025Pau Vilimelis Aceituno, Jack William Miller, Noah Marti +2Long-Term Time Series Forecasting Time Series Forecasting
Institute of Neuroinformatics, ETH Zürich and University of Zürich, Winterhurerstrasse 190, Zürich 8057, Switzerland · School of Computing, National Australian University, 108 North Rd, Acton ACT 2601, Australia · Unit of Land Change Science, Swiss Federal Research Institute for Forest, Snow and Landscape Zürcherstrasse 111, Birmensdorf 8903, Switzerland
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
May 23, 2025Hao Wang, Licheng Pan, Zhichao Chen +5Long-Term Time Series Forecasting Time Series Forecasting
Xiaohongshu Inc. · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · Gaoling School of Artificial Intelligence, Renmin University of China +4
Markovian Linguistic-Temporal Bridge: Unlocking the Potential of LLMs for Time Series Forecasting
May 19, 2025Siming Sun, Kai Zhang, Xuejun Jiang +2Time Series Forecasting LLM-Based Time Series Forecasting
Zhejiang University
FANTF: Fuzzy attention network based transformers for time series analysis
Mar 31, 2025Sanjay Chakraborty, Fredrik HeintzTime Series Transformers Time Series Forecasting
Department of Computer and Information Science (IDA), REAL, AIICS, Linköping University, Linköping, Sweden
Fewer yet critical: Reducing Redundant Token Dependencies for Transformer-based Time Series Forecasting
Mar 10, 2025Jianqi Zhang, Yuchan Liu, Zeen Song +2Time Series Forecasting Transformer Attention
National Key Laboratory of Space Integrated Information Systems, Institute of Software, Chinese Academy of Sciences, Beijing, China 1 · University of Chinese Academy of Sciences, Beijing, China 2 · Digital-Intelligent Operations Center, Guizhou Power Grid Co., Ltd., Guizhou, China 3 +1
Foreclassing: From Forecasts to Decisions
Mar 6, 2025Daniel Andrew Coulson, Martin T. WellsTime Series Classification Time Series Forecasting
Department of Statistics and Data Science, Cornell University
Surrogate Modeling for Explainable Predictive Time Series Corrections
Dec 27, 2024Alfredo Lopez, Florian SobieczkySurrogate Modeling Explainable Time Series Forecasting
Software Competence Center Hagenberg, Hagenberg, Austria.
Koopman-informed recurrent neural networks
Oct 30, 2024Erik Lien Bolager, Ana Čukarska, Iryna Burak +2Neural Network Optimization Time Series Forecasting
Munich Data Science Institute · School of Computation, Information and Technology, Technical University of Munich, Germany · Interdisciplinary Center for Scientific Computing (IWR) +1

Feb 2, 2026J/K move · Enter open · S save

Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting

Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le

Applied Artificial Intelligence Initiative Deakin University Geelong, Australia

Multivariate Time Series Forecasting Frequency-Domain Time Series Modeling Time Series Forecasting Multimodal Fusion Multiscale Time-Series Modeling

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Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals. The core challenge is to effectively combine temporal numerical patterns with the context embedded in other modalities, such as text. While most existing methods align textual features with time-series patterns one step at a time, they neglect the multiscale temporal influences of contextual information such as time-series cycles and dynamic shifts. This mismatch between local alignment and global textual context can be addressed by spectral decomposition, which separates time series into frequency components capturing both short-term changes and long-term trends. In this paper, we propose SpecTF, a simple yet effective framework that integrates the effect of textual data on time series in the frequency domain. Our method extracts textual embeddings, projects them into the frequency domain, and fuses them with the time series' spectral components using a lightweight cross-attention mechanism. This adaptively reweights frequency bands based on textual relevance before mapping the results back to the temporal domain for predictions. Experimental results demonstrate that SpecTF significantly outperforms state-of-the-art models across diverse multi-modal time series datasets while utilizing considerably fewer parameters. Code is available at https://github.com/hiepnh137/SpecTF.