Time Series Generation

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian
May 19, 2026Wenpin Tang, Nizar Touzi, Zikun Zhang +1Stochastic Differential Equations Time Series Generation
GenTS: A Comprehensive Benchmark Library for Generative Time Series Models
May 18, 2026Chenxi Wang, Xiaorong Wang, Peiyang Li +1Time Series Generation Forecasting Benchmarks
The University of Hong Kong Hong Kong SAR, China · Fudan University Shanghai, China
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
May 16, 2026Yangyou Liu, Zezhi Shao, Xinyu Chen +3OOD Generalization Time Series Generation
College of Computer Science, Sichuan University, Chengdu, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Institute of Artificial Intelligence, University of Central Florida, Orlando, USA +1
Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
May 13, 2026Elias Reich, Saverio Messineo, Stefan HuberTime Series Generation Gaussian Processes
Josef Ressel Centre for Intelligent and Secure Industrial Automation Salzburg University of Applied Sciences, Salzburg, Austria
SDFlow: Similarity-Driven Flow Matching for Time Series Generation
May 7, 2026Wei Li, Shibo Feng, Pengcheng Wu +3Flow Matching Time Series Generation
Shanghai Jiao Tong University · Shanghai University · Nanyang Technological University +3
TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
May 2, 2026Seokhyun Lee, Jaeho Kim, Changjun Oh +2Time Series Generation Multiscale Time-Series Modeling
Department of Artificial Intelligence, Korea University, Seoul, South Korea · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
Preserving Temporal Dynamics in Time Series Generation
Apr 29, 2026Ci Lin, Futong Li, Tet Yeap +1Time Series Generation Time Series Data Augmentation
Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
Apr 29, 2026Zhangzhi Xiong, Haoyi Wu, You Wu +3Transformer Conditional Random Fields
School of Information Science and Technology, ShanghaiTech University
Accelerating Frequency Domain Diffusion Models with Error-Feedback Event-Driven Caching
Apr 24, 2026Dong Liu, Haisheng Wang, Yanxuan YuTime Series Generation Diffusion Model Caching
University of California, Los Angeles · Yale University · East China Normal University +1
Synthetic data in cryptocurrencies using generative models
Apr 17, 2026André Saimon S. Sousa, Otto Pires, Frank Acasiete +3Synthetic Data Generation Time Series Generation
Universidade SENAI CIMATEC Salvador, Brazil · CoFi, Universidad Jorge Tadeo Lozano Bogotá, Colombia · Universidade do Estado da Bahia - UNEB Salvador, Brazil
Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling
Apr 14, 2026Pierre Lotte, André Péninou, Olivier TesteTime Series Generation Anomaly Localization
EPE UT, IRIT · UT2J, IRIT-SIG, IRIT · IRIT-SIG, IRIT, UT2J, Comue de Toulouse
SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series
Apr 8, 2026Alexandre Alouadi, Grégoire Loeper, Célian Marsala +2Time Series Generation Financial Time Series
BNPP and Ecole Polytechnique. · BNPP and Monash University · BNPP and ENSAE Paris +2
Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
Apr 6, 2026Umang Dobhal, Christina Garcia, Sozo InoueTime Series Generation Tabular Diffusion Models
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Apr 6, 2026Annita Vapsi, Penghang Liu, Saheed Obitayo +8Synthetic Data Generation Time Series Generation
JPMorganChase
MR-ImagenTime: Multi-Resolution Time Series Generation through Dual Image Representations
Mar 30, 2026Xianyong Xu, Yuanjun Zuo, Zhihong Huang +4Time Series Forecasting Multiscale Time-Series Modeling
Department, State Grid Hunan Electric Power Company Limited Research Institute & Hunan Province Engineering Technology Research Center of Electric Power Multimodal Perception and Edge Intelligence, Changsha, Hunan, China. · College of Computer Science, Hunan University, Changsha, Hunan, China.
Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data
Mar 28, 2026Shijie ZhangTime Series Generation Multimodal Generation
School of Computer Science and Engineering, Northeastern University, Shenyang, China
Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs
Mar 20, 2026Shaoshuai Du, Joze M. Rozanec, Andy Pimentel +1Time Series Generation Conditional Generative Modeling
Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands · Computer Architecture for Embedded Systems, University of Twente, Enschede, The Netherlands · Laboratory of Artificial Intelligence, Institute Jozef Stedan, Ljubljana, Slovenia
Variance-Corrected Multi-Asset Equity Simulation with Hybrid Hidden Markov Marginals
Mar 10, 2026Abdulrahman Alswaidan, Jeffrey D. VarnerQuantitative Finance Synthetic Data Generation
Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850
ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
Mar 5, 2026Shaocheng Lan, Shuqi Gu, Zhangzhi Xiong +1Time Series Generation Synthetic Data Evaluation
School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
TimeOmni-VL: Unified Models for Time Series Understanding and Generation
Feb 19, 2026Tong Guan, Sheng Pan, Johan Barthelemy +5Time Series Generation Time-Series Foundation Models
Griffith University · Zhejiang University · NVIDIA +4
Is Flow Matching Just Trajectory Replay for Sequential Data?
Feb 9, 2026Soon Hoe Lim, Shizheng Lin, Michael W. Mahoney +1Flow Matching Time Series Generation
Department of Mathematics, KTH Royal Institute of Technology · Nordita, KTH Royal Institute of Technology and Stockholm University · Department of Statistics, University of California at Berkeley +2
Synthetic Time Series Generation via Complex Networks
Jan 30, 2026Jaime Vale, Vanessa Freitas Silva, Maria Eduarda Silva +1Time Series Generation Synthetic Data Evaluation
Faculdade de Ciˆencias, Universidade do Porto · INESC TEC-CRACS · Faculdade de Economia, Universidade do Porto +1
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Jan 20, 2026Xu Zhang, Junwei Deng, Chang Xu +2Irregular Time-Series Modeling Neural Controlled Differential Equations
Microsoft Research, Beijing, China · University of Illinois Urbana-Champaign, Illinois, United States · Imperial College London, London, United Kingdom
EEGDM: Learning EEG Representation with Latent Diffusion Model
Aug 28, 2025Shaocong Wang, Tong Liu, Yihan Li +6Electroencephalography Latent Diffusion Models
Tsinghua University · Tianjin University
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Irregular Time-Series Modeling Stochastic Differential Equations
Department of Mathematics, Humboldt-Universität zu Berlin, 12489, Berlin, Germany · Department of Mathematics, Technische Universität Berlin, 10623, Berlin, Germany · Bundesanstalt für Materialforschung und -prüfung, 12205, Berlin, Germany +1

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Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian

Wenpin Tang, Nizar Touzi, Zikun Zhang, Xun Yu Zhou

Stochastic Differential Equations Time Series Generation Score-Based Generative Modeling Diffusion Models

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Diffusion models have achieved remarkable success in generating samples from unknown data distributions. Most popular stochastic differential equation-based diffusion models perturb the target distribution by adding Gaussian noise, transforming it into a simple prior, and then use denoising score matching, a consequence of Tweedie's formula, to learn the score function and generate clean samples from noise. However, non-Gaussian diffusion models with state-dependent diffusion coefficient have been largely underexplored, as have the corresponding Tweedie's formulae. In this work, we extend Tweedie's formula to important non-Gaussian processes, including geometric Brownian motion (GBM), squared Bessel (BESQ) processes, and Cox-Ingersoll-Ross (CIR) processes, thereby yielding the corresponding denoising score-matching objectives. We then apply the derived formulae to image and financial time series generation using GBM- and CIR-based diffusion models, and to empirical Bayes estimation under the BESQ setting. The reported experimental results demonstrate the potential of non-Gaussian models.