LLM-Based Time Series Forecasting

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Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?
Oct 5, 2026Nathaniel T. Hindman, Fabricio MuraiTime Series Forecasting LLM-Based Time Series Forecasting
Artificial Intelligence Worcester Polytechnic Institute Worcester, Massachusetts · Data Science Worcester Polytechnic Institute Worcester, Massachusetts
FreSia: Frequency-Semantic Instantiation and Alignment for Multivariate Time Series Analysis
Oct 5, 2026Yubo Wang, Hui He, Hezhe Qiao +2Multivariate Time Series Forecasting Time Series Forecasting
School of Computer Science and Technology, Beijing Institute of Technology, No.5 Zhongguancun South Street, Beijing, 100081, China · School of Computing and Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
Sep 30, 2026Tony Chen, Timo Stoffregen, Maxwell Xu +8Temporal Reasoning in Language Models Time Series Forecasting
Columbia University, USA · Stanford University, USA · Aionic Labs, Switzerland +4
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Sep 29, 2026Donguk Kwon, Wooseok Jeong, Dongha LeeTime Series Forecasting LLM-Based Time Series Forecasting
Yonsei University · Konkuk University
Can Language Models Learn to Forecast Stock Prices
Sep 29, 2026Jiacheng Guo, Suozhi Huang, Shuzhen Li +11Financial Forecasting LLM-Based Time Series Forecasting
Princeton University · InclusionAI · Harvard University +1
RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting
Sep 27, 2026Tong Liu, Lanmiao Liu, Xiang HuFinancial Forecasting Time Series Forecasting
Zircon Security · Utrecht University · The Max Planck Institute for Psycholinguistics +1
TimeBraid: Unifying Time Series and Language for Understanding and Forecasting
Sep 24, 2026Xinyue Wang, Jiacheng Pang, Kun Zhou +7Unified Multimodal Models Multimodal Pretraining
University of California San Diego · Aether AI · University of Southern California
Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting
Sep 14, 2026Tamanna Kumavat, Georg Brunner, Kyriakos FlourisTime Series Forecasting LLM-Based Time Series Forecasting
University of Zurich, Switzerland · Computer Vision Laboratory, ETH Zurich, Switzerland · MRC Biostatistics Unit, University of Cambridge, United Kingdom
STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting
Sep 14, 2026Jiawen Chen, Qi Shao, Yongjian Chang +3Traffic Flow Estimation LLM-Based Time Series Forecasting
School of Mathematics, Southeast University, Nanjing 210096, China · School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China · Jiangsu Province Center for Applied Mathematical Sciences, Nanjing 210096, China
Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
Sep 12, 2026Jihoon Kwon, Lawrence Liu, Daekyung Park +15Financial Forecasting Time Series Forecasting
LinqAlpha · Carbon Arc · Kalshi +7
Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models
Sep 11, 2026Ken Chen, Maneesha Perera, Wei Wang +3Multivariate Time Series Forecasting Multimodal Large Language Models
Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia · Department of Mechanical Engineering, Sri Lanka Institute of Information Technology, Sri Lanka
A Dynamic Fusion Large Language Model for Traffic Flow Prediction
Sep 11, 2026Xue Qiu, Jianli XiaoIntelligent Transportation Systems Traffic Flow Estimation
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
It's All in the Way You Say It: The Role of Information Representation in LLM-Based Glycemic-Event Prediction
Sep 8, 2026Andrea Apicella, Pasquale Arpaia, Matteo Orefice +2LLM Prompting Blood Glucose Forecasting
Department of Information Engineering, Electrical Engineering, and Applied Mathematics (DIEM), University of Salerno, Fisciano (SA), Italy · Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
Sep 2, 2026Seungyoon Choi, Hyunchul Kim, Jae-Gil Lee +1Time Series Forecasting LLM-Based Time Series Forecasting
KAIST Daejeon, Republic of Korea
Can LLMs Take the Pulse of the Economy? A Real-Time Evaluation of LLM Nowcasts on Macroeconomic Indicators
Aug 31, 2026Xinyue Zhao, Ruiyi Zhang, Liqin Ye +3Time Series Forecasting LLM Agent Evaluation
Georgia Institute of Technology · University of California San Diego
LLMODE: Aligning ODEs with LLMs via Gated Token Injection for Irregular Spatio-Temporal Forecasting
Aug 30, 2026Di Zhang, Jingyang Zhang, Ziqian Wang +4Irregular Time-Series Modeling Zero-Shot Time Series Forecasting
Beihang University Beijing, China
FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting
Aug 12, 2026Rentao Gu, Yihang Ding, Junjie Li +5Zero-Shot Time Series Forecasting Time Series Forecasting
Beijing University of Posts and Telecommunications, Beijing, 100876, China · China Telecom Research Institute, Beijing, 100032, China
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
Financial Numerical Prediction and Allocation as Token Generation
Aug 10, 2026Xu Ouyang, Moontae LeeQuantitative Finance Financial Forecasting
University of Illinois Chicago
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
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
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Aug 3, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Explainable Time Series Forecasting Time Series Reasoning
LG AI Research
LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models
Jul 27, 2026Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen +2Time Series Forecasting LLM-Based Time Series Forecasting
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting
Jul 27, 2026Dorothy Torres, Wei Cheng, Henan HuangSelective Prediction Financial Forecasting
School of Science, Technology, Engineering and Mathematics 263 Academy Street, Room S204 Jersey City, NJ 07306 · School of Electrical Engineering and Computer Science The Pennsylvania State University 207 Electrical Engineering West University Park, PA 16802, USA
LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
Jul 7, 2026Youcheng Zong, Runda Jia, Mingxuan Ren +1Time Series Forecasting LLM-Based Time Series Forecasting
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
Forecasting With LLMs: Improved Generalization Through Feature Steering
Jun 25, 2026Humzah Merchant, Bradford LevyLanguage Model Steering LLM Interpretability
University of Chicago, Illinois, United States.
Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting
Jun 25, 2026Defu Cao, Zijie Lei, Muyan Weng +2Time Series Forecasting LLM-Based Time Series Forecasting
University of Southern California · Meta · Google DeepMind
MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios
Jun 23, 2026Patara Trirat, Jin Myung Kwak, Jay Heo +2Quantitative Finance Financial Forecasting
KAIST
Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting
Jun 22, 2026Falguni Ghosh, Vahid Hashemi, Bernhard KainzLong-Term Time Series Forecasting Probabilistic Forecasting
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, 91052, Germany · AUDI AG, Auto-Union-Straße 1, 85057 Ingolstadt, Germany · Imperial College London, London SW7 2RH, United Kingdom
Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking
Jun 21, 2026Mao Guan, Qian ChenLearning to Rank Financial Forecasting
Independent Researcher.

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?

Nathaniel T. Hindman, Fabricio Murai

Artificial Intelligence Worcester Polytechnic Institute Worcester, Massachusetts · Data Science Worcester Polytechnic Institute Worcester, Massachusetts

Time Series Forecasting LLM-Based Time Series Forecasting Spatiotemporal Forecasting

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Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-economic indicators such as economic disparity, political stability, and geographic distance. This work investigates whether Large Language Models (LLMs) can improve migration forecasting by extracting contextual migration-related signals from news articles and incorporating them into a weighted Lasso forecasting framework through feature-specific regularization penalties. The proposed framework uses hierarchical LLM inference pipelines to classify migration-related push--pull signals from news data and evaluates the resulting forecasting performance across multiple migration corridors between November 2021 and November 2022, including Mexico--United States, Ukraine--Poland, and Syria--Turkey. Experimental results showed mixed performance across migration corridors and modeling strategies, and no single regularization approach consistently outperformed the others across all experiments. The best-performing Mexico configuration, which consisted of a gravity-based model augmented with the proposed push--pull ratios, achieved a Mean Absolute Percentage Error (MAPE) of 17.15%, while the strongest Syria configuration achieved a MAPE of 29.29% using Direct LLM-Lasso. For Ukraine, the best-performing configuration used LLM-Assisted Regularization (AR) and achieved a MAPE of 41.05%. Overall, the results suggest that contextual article-derived features and LLM-guided regularization can improve migration forecasting under certain conditions, although migration corridor characteristics, article volume, and hyperparameter configuration strongly influenced performance.

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