Time Series Generation

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Overview

Time series generation studies how to create realistic sequences that reproduce trends, periodicity, cross-variable dependencies, irregular observation patterns, and rare events. Common approaches include diffusion and flow-matching models, GANs and VAEs, autoregressive token models, and continuous-time methods such as Gaussian processes and neural differential equations; spectral and wavelet representations can help preserve fine and long-range structure. Generated data can augment forecasting and classification, support privacy-sensitive sharing, and enable controlled simulations in fields such as medicine, finance, energy, and neuroscience. A central challenge is balancing distributional realism and controllability with faithful temporal dynamics, extremes, and missingness, while evaluation increasingly considers downstream utility and privacy as well as statistical similarity.

Latest papers

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97 papers13 papers in the last four weeks, up 18% on the four weeks before. 0.1% of all new papers.

  1. Grid-Oriented Probabilistic Load Forecasting for AI Data Centers

    Nov 6, 2026Qingyi Yao, Ding Lin, Tianqiao Zhao +2Electricity Load ForecastingProbabilistic Time Series Forecasting

    Southern Methodist University, Dallas, TX, USA · The University of Texas at Arlington, Arlington, TX, USA · Brookhaven National Laboratory, Upton, NY, USA

  2. Composable Causality: A Toolkit for Systematic Time-Series Causal Discovery and Treatment-Effect Benchmarking

    Nov 6, 2026Muhammad Hasan Ferdous, Md Osman GaniTime-Series Causal DiscoveryTime Series Generation

    Causal AI Lab, Department of Information Systems, University of Maryland, Baltimore County

  3. UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation

    Nov 3, 2026Shibo Feng, Wei Li, Min Wu +2Time Series GenerationFlow-Based Generative Modeling

    College of Computing and Data Science, Nanyang Technological University, Singapore. · Shanghai Jiao Tong University and Beijing Zhongguancun Academy, Shanghai, China. · Institute for Infocomm Research and AIMfg, A* STAR, Singapore 138634. +1

  4. Structure, Dimensionality, and Distribution Shape - What Makes Pretraining Data Transferable, and Why the Input Pipeline Decides Which

    Nov 3, 2026Sangyoon Bae, Jiook ChaTransfer LearningSynthetic Data Pretraining

    Department of Psychology, Seoul National University, Seoul, 08826, South Korea

  5. SynForecast: Controlled Synthetic Time Series for Forecasting Research

    Nov 2, 2026Olivier Sprangers, Cristian Challu, Jan Rathfelder +2Time Series GenerationSynthetic Data Pretraining

    Nixtla, San Francisco, CA, United States

  6. Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants

    Nov 1, 2026Jiayu Huang, Boyang Xu, Yongming Liu +1Time Series GenerationPredictive Maintenance

    Arizona State University, Tempe, AZ, USA

  7. MarkovTS: Pretrained State-Transition Representations for Zero-Shot Time Series Generation

    Oct 31, 2026Yujie Li, Zezhi Shao, Jingbo Zhou +3Time Series GenerationConditional Generative Modeling

    State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · Baidu Frontier Research Department, Beijing, China

  8. Conditional Neural SDEs for Financial Time-Series Generation

    Oct 30, 2026Sina Molavipour, Alireza M. Javid, Björn Löfdahl +1Time Series GenerationStochastic Differential Equations

  9. Generated storm lifecycles fill the rare-event tail of short rainfall records

    Oct 30, 2026Valerie Tsao, Nathaniel W. Chaney, Manolis Veveakis +1Time Series GenerationSpatiotemporal Forecasting

    Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA.

  10. Conditional Diffusion Generation of DVL and Inertial Signals for Underwater Navigation

    Oct 25, 2026Michal Levin, Itzik KleinSynthetic Data GenerationAutonomous Underwater Navigation

    University of Haifa, Abba Khoushy Ave 199, Haifa, 3498838, Israel

  11. ORBIT: Autoregressive Time Series Generation via Next Frequency-Band Prediction

    Oct 19, 2026Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +5Time Series GenerationFrequency-Domain Time Series Modeling

    School of Data Science and Engineering, East China Normal University, Shanghai, China · Chang’an University, Xi’an, China

  12. FracTS: Hierarchical and Autoregressive Time Series Generation

    Oct 18, 2026Jiayu Li, Umair Afzal, Zilong Zhao +3Time Series GenerationAutoregressive Generation

    University of Illinois Urbana-Champaign · Betterdata AI · National University of Singapore

  13. Cross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation

    Oct 12, 2026Lin Jiang, Rongchao Xu, Guang WangTime-Frequency AnalysisTime Series Generation

    Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA

  14. IND-Eval: Aligning Evaluation and Simulation for Industrial Time Series Foundation Models

    Oct 12, 2026Vebjørn Haugland, Pavel Gulin, David Foss +5Time Series ForecastingTime-Series Foundation Models

    Cognite

  15. MontageFlow: Physically Valid High Fidelity EEG Generation

    Oct 10, 2026Sudarshan Regmi, Nabaraj Subedi, Arvind Pillai +6EEG Signal GenerationTime Series Generation

    Dartmouth College

  16. Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting

    Oct 7, 2026Guoxiong Long, Huizhen Huang, Qikun Cai +2Synthetic Data EvaluationSynthetic Data Generation

    School of Computer and Big Data, Minjiang University, No. 200 Xiyuangong Road, Fuzhou, 350108, Fujian, China

  17. Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals

    Oct 6, 2026Jaedong HwangElectroencephalographyEEG Signal Generation

    Massachusetts Institute of Technology

  18. HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation

    Oct 5, 2026Perrine Chassat, Agathe GuillouxStochastic Differential EquationsSynthetic Data Generation

    Inria, Université Paris Cité, Inserm, HeKA F-75015 Paris, France

  19. Image Synthesis as an Intermediate for Controllable Time Series Generation

    Oct 4, 2026Haochen Yuan, Jing Xie, Yunbo WangTime Series ForecastingTime Series Generation

    MoE Key Lab of Artificial Intelligence, Institute of AI, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China

  20. Wavelet Flow Matching for Time Series

    Sep 30, 2026Lucas Poinsignon, Jorge da Silva Goncalves, Samuel Ruiperez-Campillo +1Flow MatchingTime Series Generation

    Dept. of Computer Science ETH Zurich, Switzerland

  21. Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

    Sep 29, 2026Zilong Zhao, Abdul Raheem, Jiayu Li +5Constrained Generative ModelingSynthetic Data Generation

    Betterdata AI · National University of Singapore · University of Illinois Urbana-Champaign +1

  22. HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling

    Sep 28, 2026Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +2Variational AutoencodersTime Series Generation

    School of Data Science and Engineering, East China Normal University, Shanghai, China

  23. Marginal Calibration Masks Generator Differences in Held-Out Evaluation of Multivariate Time Series, with an Application to ERA5 Weather Sequences

    Sep 27, 2026Mustafa Özaytaç, Özge Karadağ AtaşTime Series GenerationModel Calibration

    Department of Statistics, Graduate School of Science and Engineering, Hacettepe University, Ankara, 06800, Turkey · Department of Statistics, Faculty of Science, Hacettepe University, Ankara, 06800, Turkey

  24. ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation

    Sep 27, 2026Changhun Kim, Sunguk Jang, Jeongjun Lee +5Irregular Time-Series ModelingFlow Matching

    University of Wisconsin–Madison · AITRICS · KAIST

  25. ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation

    Sep 21, 2026Subo Wei, Jianqi Gao, Mingyan Fan +3Time Series GenerationAutoregressive Generation

    School of Computer Engineering and Science, Shanghai University, Shanghai, China · Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

  26. Time series generation with spectrally aligned latent flow matching

    Sep 18, 2026Camilo Carvajal Reyes, Felipe TobarTime Series GenerationFlow-Based Generative Modeling

    Department of Mathematics, Imperial College London, 180 Queen’s Gate, London SW7 2HR, United Kingdom

  27. LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing

    Sep 14, 2026Mantek Singh, Jeshwanth Challagundla, Prateek Karnal +3ManufacturingSynthetic Data Generation

    Liverpool John Moores University Liverpool, England · University of Texas at Arlington Texas, USA · IIT Patna Patna, India +3

  28. CodeTS: Verifiable Text-to-Time Series Generation via Executable Code

    Sep 14, 2026Xudong Yuan, Shunyu Liu, Tongya Zheng +3Time Series Generation

    Zhejiang University, Hangzhou, China · Nanyang Technological University, Singapore · South China University of Technology, Guangzhou, China

  29. Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling

    Sep 14, 2026Rafael Pina, Varuna De Silva, Mindula IlleperumaSynthetic Data AugmentationTime Series Generation

    Institute for Digital Technologies Loughborough University London London, United Kingdom

  30. MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis

    Sep 4, 2026Yanhao Huang, Shibo Feng, Wanjin Feng +2Flow MatchingSynthetic Data Generation

    Shanghai Jiao Tong University · Nanyang Technological University · Tsinghua University