Time Series Modeling

Latest papers

51 papers14 papers in the last four weeks, up 56% on the four weeks before. 0.1% of all new papers.

CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries
Oct 19, 2026Shusen Ma, Xudong Zuo, Yangchun Xie +1Multivariate Time Series Forecasting Time Series Modeling
Institute of Advanced Technology University of Science and Technology of China Hefei, China · Department of Automation University of Science and Technology of China Hefei, China
Learning volatility-adaptive forgetting via GARCH-modulated LSTM for stock price forecasting
Oct 17, 2026Xin-Rui Lu, Da-Qing ZhangTime Series Forecasting Time Series Modeling
School of Science, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, PR China
Towards a GraphNARX model for structural dynamics
Oct 16, 2026George Tsialiamanis, Keith WordenNonlinear System Identification Time Series Modeling
Dynamics Research Group, School of Mechanical, Aerospace, and Civil Engineering, University of Sheffield, Sheffield, Mappin St, Sheffield, United Kingdom
Time-partitioning Gaussian Process Mixture Model
Oct 14, 2026Guilherme Piantino, Hedibert F. LopesTime Series Modeling Non-Stationary Time Series
Insper Institute of Education and Research, São Paulo
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Oct 14, 2026Chengyi Pan, Huangqimei Zheng, Hao XueTime Series Modeling Exogenous Time Series Forecasting
UGOD, HKUST-GZ · School of Software, Yunnan University · UGOD, The Hong Kong University of Science and Technology (Guangzhou)
ChronoScribe: Semantic-Numerical Specialization for Instruction-Driven Time Series Forecasting
Oct 12, 2026Yuchen Jiao, Miao Zhang, Yuxuan Liu +1Time Series Forecasting LLM-Based Time Series Forecasting
Beijing University of Posts and Telecommunications
Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories
Oct 7, 2026Emanuele Albini, Francesca Toni, Saumitra Mishra +1Time Series Forecasting Time Series Modeling
J.P. Morgan AI Research Imperial College London London, UK · Imperial College London London, UK · J.P. Morgan AI Research London, UK
StaFIR: Convex Learning of Stationarity-Aware Causal Filters
Oct 5, 2026Lorena Egger, Mathis LingerTime Series Forecasting Financial Time Series
Drakai Capital Paris
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 LLM-Based Time Series Forecasting
Columbia University, USA · Stanford University, USA · Aionic Labs, Switzerland +4
ZeroDiff: Zero-Shot Time Series Reconstruction via Informed-Prior Diffusion
Sep 29, 2026Yingda Fan, Dan Lu, Xiaowei JiaTime Series Modeling Diffusion Models
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA · Oak Ridge National Laboratory, Oak Ridge, TN, USA
Dynamical Parameters: An Interpretability Framework for Time-Series Foundation Models
Sep 28, 2026Kang Yang, Gaofeng Dong, Liying Han +1Explainable Time Series Forecasting Representation Probing
Department of Electrical and Computer Engineering University of California, Los Angeles
Correct then Forecast: Observer State-Space Models for Time Series Forecasting
Sep 27, 2026Alexis-Raja Brachet, Guillaume Clavier--Frémond, Abdelhakim Ziani +2Latent Dynamics Modeling Time Series Forecasting
MICS, CentraleSup´elec, Universit´e Paris-Saclay, France · CentraleSup´elec, IETR UMR CNRS 6164, France · Universit`a di Torino, Torino, Italy.
DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling
Sep 27, 2026Yiqiu Liu, Siru Zhong, Zhiguang Wang +2Time Series Decomposition Non-Stationary Time Series
The Hong Kong University of Science and Technology (Guangzhou), China · Abel AI Lab, USA · Squirrel Ai Learning, USA
EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting
Sep 25, 2026Seunghan Lee, Sangjun Han, Jun Seo +9Time Series Forecasting Time Series Modeling
LG AI Research
Forecast Workflow Bench: Evaluating Language-Model Decisions with Budgeted Forecast Tools
Sep 23, 2026Shunya NagashimaForecasting Benchmarks LLM Tool Use
Neurogica Inc.
Leaky-integrator reconstruction: taming error accumulation in recursive differenced time-series forecasting
Sep 20, 2026Zijiang YangTime Series Forecasting Time Series Modeling
New York University, Tandon School of Engineering, Brooklyn, NY 11201
SOTER: A Generative Time-Series Foundation Model for Wearable Human Physiological Signals
Sep 15, 2026Fangke Chen, Sirry Chen, Wei Chen +1Irregular Time-Series Modeling Time Series Imputation
School of Integrated Circuits, Zhejiang University Hangzhou, China · Shanghai Innovation Institute Shanghai, China · School of Data Science, Fudan University Shanghai, China +1
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
Rolling Day-Wise Mortality Prediction in Critically Ill Patients With AKI on CRRT Utilizing Machine Pressure Waveforms
Sep 11, 2026Shehan Irteza Pranto, Joanna Yang, Joshua Lambert +6Time Series Modeling Mortality Prediction
Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, AL, USA · Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai, New York, NY, USA · College of Nursing, University of Cincinnati, OH, USA +4
Explaining Time Series Forecasting with Horizon-Resolved Attribution
Sep 11, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Explainable Time Series Forecasting Feature Attribution
LG AI Research
Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model
Sep 5, 2026Roberto TacconelliPredictive Coding Time Series Modeling
Independent Researcher
Liquid Gated Attention
Aug 31, 2026Yiheng Jiang, Yuanbo Xu, Yongjian YangGated Attention Time Series Modeling
Mobile Intelligent Computing Laboratory, College of Computer Science and Technology, Jilin University, China
Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting
Aug 28, 2026Morad Laglil, Younes Hlal, Marouane El Hadari +2Time Series Forecasting Long-Term Time Series Forecasting
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France · Savoye, 21600 Longvic, France
Causal Analysis for Time Series Foundation Models
Aug 25, 2026Mathis Jander, Wouter van Heeswijk, Martijn MesTime-Series Foundation Models Time Series Modeling
Faculty of Behavioral, Management and Social Sciences, University of Twente, Enschede, Netherlands · European Central Bank, Frankfurt am Main, Germany
Lévy Attention: Single-Pass Predictive Uncertainty for Continuous-Time Attention
Aug 19, 2026Sotirios P. Chatzis, Loukas PapadoulasTime Series Modeling Uncertainty Quantification
GenML Laboratory, Department of Electrical Engineering and Computer Science and Engineering, Cyprus University of Technology, Limassol, Cyprus · Ethical AI Novelties, Cyprus
Scalable estimation of VARMA models
Aug 6, 2026Daniel Paulin, Victor ElviraMultivariate Time Series Forecasting Parameter Estimation
College of Computing & Data Science Nanyang Technological University Singapore · School of Mathematics University of Edinburgh Edinburgh, UK
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
Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework
Jul 30, 2026Tianen Shen, Zhengyu Li, Yutong Li +4Irregular Time-Series Modeling Multivariate Time Series Forecasting
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
Rethinking Multimodal Time-Series Forecasting Evaluation
Jul 8, 2026Haoxin Liu, Yichen Zhou, Rajat Sen +2Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
Georgia Institute of Technology · Google Research

Oct 19, 2026J/K move · Enter open · S save

CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries

Shusen Ma, Xudong Zuo, Yangchun Xie, Yun-Bo Zhao

Institute of Advanced Technology University of Science and Technology of China Hefei, China · Department of Automation University of Science and Technology of China Hefei, China

Multivariate Time Series Forecasting Time Series Modeling Time Series Forecasting Irregular Time-Series Modeling

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Irregular multivariate time series forecasting (IMTSF) requires capturing temporal dynamics and cross-variable dependencies from sparse, asynchronous observations. Existing methods often rely on learned temporal encoders, coupling historical representation construction with model optimisation and repeatedly processing the same observations. We propose CTLite, a lightweight forecasting framework that decouples historical summarisation from learned prediction. Specifically, a parameter-free continuous-time moment encoder summarises local levels and trends at multiple temporal scales while suppressing poorly supported slope estimates. A compact readout combines damped local extrapolation with a low-rank cross-variable correction that accounts for observation freshness, target availability, and forecast horizon. Since the historical summaries are independent of trainable parameters and forecast queries, they can be reused across optimisation steps and repeated predictions for an unchanged history without altering predictions or parameter gradients. Experiments on four real-world benchmarks show that CTLite achieves the best or second-best mean MSE and MAE among the evaluated methods with only 88-2,537 trainable parameters. Under matched GPU workloads at batch size 32, CTLite reduces inference latency by 18.6-87.3% compared with the latest models, including the cost of historical encoding. Reusing summaries further reduces total runtime by approximately 52% over 16 forecast calls on the same history compared with recomputing the encoder for every call, including the initial encoding cost. The code is at: https://github.com/SSMa913/NICLab-CTLite.