Time Series Forecasting

Follow this topic Ask about this topic

Latest papers 667

139 papers in the last four weeks, up 101% on the four weeks before. 0.9% of all new papers.

All topics

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting
Aug 10, 2026Xu Zhang, Chang Xu, Hui Sun +5RL for Language Models Explainable Time Series Forecasting
Fudan University Shanghai, China · Microsoft Research Beijing, China · Jilin University Jilin, China
Evaluating Generative Time-Series Models on Data with Point Masses
Aug 10, 2026Jian XuTime Series Forecasting Time Series Generation
RIKEN iTHEMS, Wako, Japan · RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
Do AI Forecast Ensembles Sample the Correct Conditional Distribution?
Aug 9, 2026Lucas J. Howard, Elizabeth A. BarnesTime Series Forecasting Probabilistic Forecasting
Boston University, Faculty of Computing and Data Sciences
Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction
Aug 9, 2026Kasun Dewage, Suranadi De Silva, Shankhadeep MondalResidual Learning Time Series Forecasting
Dept. of Mathematics University of Central Florida Orlando, FL, USA · Dept. of Computer Science University of Central Florida Orlando, FL, USA
Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Study
Aug 9, 2026Steve Cabrel Teguia Kouam, Rockefeller Rockefeller, Raoult Dabou Teukam +4Surrogate Modeling Time Series Forecasting
Department of Physics, Faculty of Science, University of Douala, Douala, PO. Box 24157, Littoral, Cameroon. · Division of Applied Mathematics, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa. · Department of Engineering, University of Quebec in Abitibi-T´emiscamingue, Quebec, Canada. +1
End-to-End Neural Decomposition with Koopman Operators for Time-Series Forecasting
Aug 9, 2026De-Yan Lu, Xugang Lu, Yu Tsao +1Koopman Operator Learning Time Series Forecasting
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · National Institute of Information and Communications Technology, Kyoto, Japan · Research Center for Information Technology Innovation, Academic Sinica, Taipei, Taiwan
A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration
Aug 9, 2026Wei Wang, Yaosen Chen, Han Yang +5World Model Learning World Models
Sobey Media Intelligence Laboratory
Efficient Test-Time Scaling for LLM-based Time Series Forecasting
Aug 9, 2026Xuan-May Le, Minh-Tuan Tran, Ling Luo +3Long-Term Time Series Forecasting Time Series Forecasting
The University of Melbourne Melbourne, Victoria, Australia · Monash University Melbourne, Victoria, Australia
Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models
Aug 8, 2026Jianqi Zhang, Xingyu Zhang, Zeen Song +3Time Series Forecasting RL Post-Training
Institute of Software, Chinese Academy of Sciences, Beijing, China
Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study
Aug 7, 2026Yushu Zou, Ye Li, Johra Moosa +3Epidemic Forecasting Gradient-Boosted Decision Trees
Public Health Ontario, Toronto, Canada · Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Canada · Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada +1
PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention
Aug 7, 2026Amal Saadallah, Julia Tjus, Petra Wiederkeher +1Time Series Forecasting Physics-Informed ML
Faculty of Physics and Astronomy, Ruhr University Bochum, Germany · Virtual Machining Group, TU Dortmund, Germnay · Faculty of Physics, Germany
QFCQT: A Chaotically Gated Quantformer Framework for Volatile Time-Series Forecasting
Aug 7, 2026Junkai Lin, Siqi Hou, Raymond LeeMultivariate Time Series Forecasting Time Series Forecasting
Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai, China · Guangdong Provincial Key Lab of Interdisciplinary Research and Application for Data Science (BNBU), Zhuhai, China
Timestep-Conditioned Transformers for Global Weather Forecasting
Aug 6, 2026Sam Levang, Fran Bartolic, Ty Dickinson +3Time Series Forecasting Probabilistic Forecasting
Salient
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
Aug 6, 2026Yixiong Xiao, Congxi Xiao, Jingbo ZhouRetrieval-Augmented Generation Time Series Forecasting
Baidu, Inc. Beijing, China
Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems
Aug 6, 2026Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath +2Irregular Time-Series Modeling Multivariate Time Series Forecasting
a School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2650, Australia · b School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, NSW 2640, Australia · c Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, NSW 2678, Australia +4
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
Aug 6, 2026Yann Fabel, Bijan Nouri, Milon Miah +4Time Series Forecasting Solar Power Forecasting
1DLR, Institute of Solar Research · 2RWTH Aachen University · 3CIEMAT, Energy Department, Renewable Energy Division
Multivariate Time Series Forecasting needs Cross Variable Loss
Aug 6, 2026Kuiye Ding, Yifan Hu, Hanchen Wang +1Multivariate Time Series Forecasting Time Series Forecasting
University of Technology Sydney · Tsinghua University · The Hong Kong University of Science and Technology (Guangzhou)
Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load
Aug 5, 2026Thomas Bartz-Beielstein, Inalbek Akiev, Lalo MohamadTime Series Forecasting Forecasting Benchmarks
THK-AI Research Cluster, Germany · TH Köln, Germany
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Aug 5, 2026Priyanka Nihalchandani, Naman Srivastava, Varun Ojha +1Mixture of Experts Time Series Forecasting
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bengaluru, India · School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
Rethinking Reservoir Pruning: A Dynamical Perspective for Echo State Networks
Aug 5, 2026Sudip Laudari, Puspa Raj AdhikariDynamic Neural Networks Time Series Forecasting
Independent Researcher. · Advanced Materials Science and Engineering, Sungkyunkwan University.
When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters
Aug 5, 2026Fangxin Wang, Ziyi Zhang, Diyi Zhuang +4Residual Learning Time Series Forecasting
University of Illinois Chicago · Texas A&M University · Massachusetts Institute of Technology +1
Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting
Aug 5, 2026Mengzhou Gao, Huangqian Yu, Pengfei JiaoDynamical Systems Long-Term Time Series Forecasting
Hangzhou Dianzi University Hangzhou, China
Amortized Interventional Forecasting for Multivariate CIR Processes
Aug 4, 2026Andreas Sauter, Sumit Sourabh, Drona Kandhai +1Causal Effect Estimation Quantitative Finance
Vrije Universiteit Amsterdam · ING · University of Amsterdam / ING +1
POEM: Phase-Aware $\mathrm{SO}(2)$ Feature Rotation for Time Series Forecasting Under Periodicity Drift
Aug 4, 2026Jiawen Zhu, Shuhan Liu, Shengxuan Li +2Time Series Forecasting
School of Software Technology, Zhejiang University · State Key Lab of CAD&CG, Zhejiang University
CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting
Aug 4, 2026Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-ThiemeLong-Term Time Series Forecasting Time Series Forecasting
ISMLL, University of Hildesheim, VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany
Traceable Multi-Agent System for Knowledge-Based Forecasting
Aug 4, 2026Junhyeok Kang, Sangjun Han, Hyeokjun Choe +1Explainable Time Series Forecasting Multi-Agent Orchestration
LG AI Research · Seoul, Republic of Korea
FinVerse: Financial Time-Series Benchmark
Aug 4, 2026Jaehoon Lee, Jun Seo, Seunghan Lee +9Benchmark Design Financial Forecasting
LG AI Research
CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
Aug 4, 2026Xiaoyu Tao, Mingyue Cheng, Bokai Pan +6Time Series Forecasting LLM-Based Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China
Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput
Aug 3, 2026Yinxiao Zhang, Sen Wang, Yi GaoTime Series Forecasting
School of Aviation and Transportation Technology, Purdue University · West Lafayette, Indiana, 47907 · Department of Geography, The Ohio State University +3
Forecasting Revenue with its Customer-Base Drivers: When and Why Coordination Helps
Aug 3, 2026Kyeongbin Kim, Daniel McCarthy, Dokyun LeeMultivariate Time Series Forecasting Financial Forecasting
University of Wisconsin–Madison · University of Maryland · Boston University

Aug 10, 2026J/K move · Enter open · S save

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

Xu Zhang, Chang Xu, Hui Sun, Nan Ma+4

Fudan University Shanghai, China · Microsoft Research Beijing, China · Jilin University Jilin, China

RL for Language Models Explainable Time Series Forecasting Time Series Reasoning Ensemble Learning Time Series Forecasting LLM-Based Time Series Forecasting

Open paper AskPDF arXiv

Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples. Ensemble learning addresses this by combining complementary model strengths, yet existing methods rely on fixed rules or black-box models based solely on numerical inputs, failing to leverage LLM reasoning for interpretable weighting decisions. We propose REATS, which leverages LLM reasoning capabilities as an intelligent ensemble router that jointly processes textual temporal pattern descriptions and numerical features to produce interpretable, sample-adaptive ensemble weights through chain-of-thought reasoning. To enable effective LLM-based ensembling, we study its key design choices and propose: (i) a structured input pipeline that transforms raw time series into hybrid textual--numerical representations with fixed token cost, enabling rule-based chain-of-thought construction without API dependency, augmented with retrieved similar-sample priors; (ii) a diverse multi-row weight supervision scheme coupled with a token-efficient percentage-table format that reduces numerical complexity and mitigates LLM hallucinations; and (iii) a two-stage fine-tuning framework combining SFT with GRPO, where a reciprocal reward mapping transforms the continuous unbounded MSE gap into bounded signals with amplified near-oracle sensitivity, addressing the uniform sensitivity and outlier-dominated advantage compression inherent in naive reward designs for regression-based GRPO. Experiments on eight benchmarks demonstrate that REATS outperforms competitive ensemble baselines while providing natural language explanations and demonstrating strong transfer learning and out-of-domain generalization to unseen candidate models.