Time Series Modeling

Latest papers

56 papers11 papers in the last four weeks, down 8% on the four weeks before. 0.1% of all new papers.

Nonparametric GARCH: An Iterated Neural Estimator
Nov 3, 2026Ruizhi Deng, Guohao Shen, Ngai Hang ChanTime Series Modeling Parameter Estimation
Department of Applied Mathematics, The Hong Kong Polytechnic University · Department of Biostatistics, City University of Hong Kong
PCD: Predictive Coding Dynamics for Irregularly Sampled Time Series
Oct 30, 2026Tianrui Gou, Jiayangmei Xu, Xingzhou Zhang +1Time Series Modeling Irregular Time-Series Modeling
School of Artificial Intelligence, Beihang University, Beijing 100191, China. · Department of Foreign Languages, Beihang University, Beijing 100191, China.
MetaARIMA: Configuration-Level Transfer Learning for ARIMA Models
Oct 25, 2026Vitor Cerqueira, Ricardo Inácio, Carlos SoaresTime Series Modeling Time Series Forecasting
University of Coimbra; CISUC/LASI – Centre for Informatics and Systems of the University of Coimbra; Coimbra, Portugal · Faculty of Engineering, University of Porto; Porto, Portugal · Faculty of Engineering, University of Porto; Laboratory for Artificial Intelligence and Computer Science (LIACC), Portugal; Fraunhofer Portugal AICOS, Portugal; Porto, Portugal
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 ZhangStock Price Forecasting Time Series Forecasting
School of Science, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, PR China
OliO: ODE-based Linear Transition Operator for Self-Supervised Time Series Forecasting
Oct 16, 2026Kwangryeol Park, Seulki LeeTime Series Forecasting Self-Supervised Time Series Representation Learning
Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, South Korea · School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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

Nov 3, 2026J/K move · Enter open · S save

Nonparametric GARCH: An Iterated Neural Estimator

Ruizhi Deng, Guohao Shen, Ngai Hang Chan

Department of Applied Mathematics, The Hong Kong Polytechnic University · Department of Biostatistics, City University of Hong Kong

Time Series Modeling Parameter Estimation Time Series Forecasting Minimax Estimation

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We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved conditional variances. Under mild conditions, we establish finite-sample bounds for the time-averaged in-sample variance-path error and show that the estimator is minimax rate-optimal up to logarithmic factors. Under stationarity and mixing conditions, we establish the same rate for recursion-map estimation and derive finite-sample bounds for out-of-sample variance forecasts and expected excess Gaussian quasi-likelihood (QLIKE) loss. The convergence rates depend on the true lag orders and remain unchanged when the fitted orders are overspecified. Simulation studies illustrate the effectiveness of our estimators under nonlinear and time-varying volatility dynamics. Real-data applications suggest that our neural methods perform competitively and can incorporate option-price and high-frequency information.