Retrieval-Augmented Time Series Forecasting

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Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters
Oct 6, 2026Chao He, Jianyu Xu, Xinyi Guo +5Non-Stationary Time Series Forecasting Retrieval-Augmented Time Series Forecasting
Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Foshan, China · School of Artificial Intelligence South China Normal University Foshan, China · Faculty of Humanities and Arts Macau University of Science and Technology Macau, China
QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities
Sep 28, 2026Hanyin Cheng, Linfeng Wang, Zhengbo Qu +7Multivariate Time Series Forecasting Multimodal Foundation Models
East China Normal University · Huawei Technologies Co., Ltd.
RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
Sep 3, 2026Yuchen He, Yueyang Cang, Zhiyuan Ning +2Multivariate Time Series Forecasting Residual Learning
Department of Automation, Tsinghua University · State Key Laboratory of Hydroscience and Engineering, Tsinghua University
Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision
Aug 24, 2026Yong-Hoon Choi, Kwang-Hyun Park, Youngjin ChoLearning to Rank Time Series Forecasting
Division of Robotics, Kwangwoon University Seoul 01897, Republic of Korea
KReF: Training-Free Retrieval for Long-Term Time-Series Forecasting and Predictive Uncertainty
Aug 7, 2026Yang Zhang, Rui SuLong-Term Time Series Forecasting Retrieval-Augmented Time Series Forecasting
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
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
Aug 6, 2026Mohammad Asadi, Soheil Hor, Bardiya Akhbari +6Zero-Shot Time Series Forecasting Retrieval-Augmented ICL
1Stanford University 2Amazon
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
TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion
Jul 31, 2026Yu Sun, Yuan Chang, Xiaohou Shi +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Beijing University of Posts and Telecommunications Beijing, China · China Telecom Research Institute Beijing, China
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
PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling
Jul 22, 2026Shiyuan Luo, Runlong Yu, Chonghao Qiu +6Time Series Forecasting Adaptive Retrieval
University of Pittsburgh · University of Alabama · University of Minnesota +3
The Spectrum Is Not Enough: When Context Helps Time-Series Forecasting
Jul 14, 2026Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan KurbanTime Series Forecasting Retrieval-Augmented Time Series Forecasting
Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
Jul 14, 2026Saiyue Lyu, Zhitian Zhang, Ruizhi Deng +1Irregular Time-Series Modeling Event Time Prediction
Department of Computer Science, University of British Columbia · Work done during an internship at RBC Borealis. · RBC Borealis
PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
Jul 9, 2026Hang Fan, Weican Liu, Ying Lu +3Time Series Forecasting Non-Stationary Time Series Forecasting
School of Economics and Management, North China Electric Power University, 102206, Beijing, China · School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, 50 Nanyang Avenue, Singapore · Department of Electrical Engineering, Tsinghua University, 100084, Beijing, China
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.
Spectral Retrieval-Augmented Time-Series Forecasting
Jun 17, 2026Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1Non-Stationary Time Series Forecasting Retrieval-Augmented Time Series Forecasting
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting
Jun 15, 2026Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R. +2Time Series Forecasting Retrieval-Augmented Time Series Forecasting
University College of Engineering, Anna University Tiruchirappalli, Tamil Nadu, India · Central University of Karnataka, India · National Institute of Technology Tiruchirappalli, India +1
Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
Jun 12, 2026Shiqiao Zhou, Zipeng Wu, Holger Schöner +3Time Series Forecasting Non-Stationary Time Series Forecasting
School of Computer Science, University of Birmingham, Birmingham, United Kingdom · School of Mathematics, University of Birmingham, Birmingham, United Kingdom · Siemens AG, Munich, Germany +1
Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors
Jun 5, 2026Tao Chen, Yexu Zhou, Zhi Gong +9Synthetic Data Pretraining Multivariate Time Series Forecasting
SUPCON Technology Co., Ltd. Hangzhou, China · Hangzhou Dianzi University, HangZhou, 310018, China · Xidian University, Xi’an 710071, Shaanxi, China.
EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
Jun 3, 2026Yiming Lu, Sihang Zeng, Zhengxu Tang +3Epidemic Forecasting Time Series Forecasting
Emory University · University of Washington
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
Jun 2, 2026Shiqiao Zhou, Holger Schöner, Zipeng Wu +3Time Series Forecasting Non-Stationary Time Series Forecasting
University of Birmingham Birmingham, United Kingdom · Siemens AG Munich, Germany
From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting
Jun 2, 2026Mingyang Liu, Qingcan Kang, Yuke Wang +6Process Reward Models Financial Forecasting
Department of Computer Science, City University of Hong Kong · Work done as an intern in Huawei Noah’s Ark Lab. · Huawei Noah’s Ark Lab
Dr-CiK: A Testbed for Foresight-Driven Agents
May 27, 2026Yihong Tang, Andrew Robert Williams, Arjun Ashok +6Time Series Forecasting Forecasting Benchmarks
McGill University · ServiceNow Research · Université de Montréal +3
ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting
May 24, 2026Jinjin Chi, Lei Feng, Yiming Wang +4Zero-Shot Time Series Forecasting Time Series Forecasting
College of Computer Science and Technology, Jilin University, Changchun, China · College of Computing and Data Science, Nanyang Technological University, Singapore
Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
May 19, 2026Liu Chong, Yingjie Zhou, Hao Li +3Time Series Forecasting Retrieval-Augmented Time Series Forecasting
College of Computer Science, Sichuan University · Computer and Information Science, University of Macau · Squirrel Ai +1
Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand
May 18, 2026Yihong Tang, Tong Nie, Junlin He +3Time Series Forecasting Spatiotemporal Forecasting
McGill University · The Hong Kong Polytechnic University · University of Toronto +1
Retrieval Mechanisms Surpass Long-Context Scaling in Time Series Forecasting
May 6, 2026Rishi Ahuja, Kumar Prateek, Simranjit Singh +1Time Series Forecasting Long-Context Modeling
Department of Information Technology, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, 144008, India.
Channel-wise Retrieval for Multivariate Time Series Forecasting
Apr 7, 2026Junhyeok Kang, Jun Seo, Soyeon Park +4Multivariate Time Series Forecasting Time Series Forecasting
LG AI Research, Republic of Korea
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
Mar 16, 2026Seunghan Lee, Jaehoon Lee, Jun Seo +7Zero-Shot Time Series Forecasting Cross-Attention
LG AI Research Seoul, South Korea
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

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Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters

Chao He, Jianyu Xu, Xinyi Guo, Ruiqi Liu+4

Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Foshan, China · School of Artificial Intelligence South China Normal University Foshan, China · Faculty of Humanities and Arts Macau University of Science and Technology Macau, China

Non-Stationary Time Series Forecasting Retrieval-Augmented Time Series Forecasting

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Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster. Most existing methods build the retrieval memory once from the training segment, leaving observations revealed after deployment unavailable as references, and generally do not calibrate how much the retrieved information should influence a frozen forecaster. We identify two key determinants of retrieval utility for a frozen forecaster: whether the history still reflects the current state, and whether the correction it induces aligns with the forecaster's residual errors, an alignment that can shift between validation and deployment when the memory becomes stale. We propose FreshCast, a plug-in retrieval framework that keeps the forecaster frozen, continuously updates a non-parametric memory with new observations, forms a memory forecast through relational kernel regression, and calibrates its weight in closed form on the validation segment. Under a simplified generative model, we characterize the optimal combination gain through the second-order relation between forecaster error and memory correction, and show that a sufficiently long look-back can make periodic memory information redundant. Across seven benchmarks and ten forecasting architectures, FreshCast reduces average MSE for every evaluated forecaster and input length, by 14.6% and 5.6% at input lengths 96 and 720, and achieves lower MSE than the evaluated retrieval-augmented and online baselines in their comparison settings. Ablations show that freezing the memory at the end of training removes most of the gain, identifying post-training observations as a primary source of improvement. For a frozen forecaster, useful historical references must remain timely and provide information that helps correct its remaining errors.

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