Time Series Generation

Latest papers 85

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

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation
Aug 4, 2026Lin Jiang, Dahai Yu, Ravikumar Gelli +1Synthetic Data Generation Time Series Generation
Department of Computer Science Florida State University Tallahassee, Florida, USA · Department of Electrical and Computer Engineering Florida State University Tallahassee, Florida, USA
Not All EEG Moments Are Equal: Position-Adaptive Time Scheduling for EEG Generation
Jul 25, 2026Boheng Liu, Ziyu Li, Chenghua Duan +2Brain-Computer Interfaces Time Series Generation
School of Computer Science and Technology Beijing Institute of Technology Beijing, China
Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods
Jul 21, 2026Seyed Mohammadreza Alavi, Ardeshir Shojaeinasab, Reza Jalayer +2Synthetic Data Generation Time Series Generation
Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran · Department of Electrical and Computer Engineering, University of Victoria, Victoria BC, Canada · FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland +1
TVGL-CFM:Generating and Forecasting Time-Varying Trajectories of Dynamic Networks with Conditional Flow Matching
Jul 18, 2026Om Roy, Yashar Moshfeghi, Keith Malcolm SmithTime Series Generation Dynamic Graph Learning
University of Strathclyde
MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion
Jul 15, 2026Yilai Liu, Shiyuan Zhang, Hongyang DuTime Series Generation User Modeling
Department of Electrical and Computer Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs
Jul 12, 2026Mohannad Takrouri, Nicolas M. Cuadrado A., Martin TakáčSynthetic Data Generation Time Series Generation
Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates
Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
Jul 5, 2026Zitao Shuai, Zongzhe Xu, Yuntian Wu +3Flow Matching Synthetic Data Generation
University of California, Los Angeles · 2Carnegie Mellon University · 3Massachusetts Institute of Technology
Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss
Jun 30, 2026Mohamed Gueye, Yazid Attabi, Manuel Morales +1Time Series Generation Generative Adversarial Networks
Croesus Lab, Croesus, Laval, Québec, Canada · Dept. of Mathematics and Statistics, University of Montreal, Montréal, Canada
Quantum Generative Diffusion Model for Real-World Time Series
Jun 25, 2026Jack Waller, Filippo Caruso, Dimitrios Makris +2Synthetic Data Augmentation Quantum Generative Modeling
School of Computer Science and Mathematics, Kingston University London, London, KT1 2EE, U.K.. · Department of Physics and Astronomy, University of Florence, Florence, Sesto Fiorentino 50019, Italy. · Department of Electronics and Electrical Engineering, Brunel University of London, London, UB8 3PH, U.K..
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
Jun 19, 2026Franco Martino O'Rourke, Ana Trisovic, Dimitris BertsimasStructural Causal Models Multivariate Time Series Classification
Massachusetts Institute of Technology, Cambridge, MA, USA.
B[FM] $^2$ : Brain Foundation Model via Flow Matching with SplitUNet
Jun 18, 2026Jaedong Hwang, Kathleen Zhang, Wei Dai +5Flow Matching EEG Decoding
Massachusetts Institute of Technology · KU Leuven
Informative Missingness to Generate Irregular Clinical Time Series
Jun 14, 2026Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora +4Synthetic Data Generation Time Series Generation
Aalborg University, Aalborg, Denmark · University of Pavia, Pavia, Italy · Bowling Green State University, Bowling Green, Ohio, USA
PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation
Jun 13, 2026Emre Yusuf, Ren Takahashi, Jayabrata BhaduriTime Series Generation Flow-Based Generative Modeling
Defense.Codes (a DBA of CapaCloud Corp
Towards a Unified Generative Model for Scarce Time Series with Domain Experts
Jun 13, 2026Zihao Yao, Qi Zheng, Jiankai Zuo +1Diffusion Transformer Time Series Generation
The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China.
Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics
Jun 10, 2026Sam Gijsen, Michał Łukomski, Marc-André Schulz +1Diffusion Transformer Time Series Generation
Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany · Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Germany +1
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Jun 9, 2026Du Yin, Hao Xue, Jinliang Deng +4Constrained Generative Modeling Time Series Generation
University of New South Wales Sydney, NSW, Australia · HKUST(GZ) China · BUAA Beijing, China
Stochastic weather generators for high-frequency wind vector time series
Jun 8, 2026Mingshi Cui, Kevin Eng, Justin T. Greene +5Time Series Generation Vector-Quantized Variational Autoencoder
Department of Statistics, Rutgers University, Piscataway, NJ, USA
Diffusion Models for Adaptive Sequential Data Generation
Jun 4, 2026Haoyang Cao, Minshuo Chen, Yinbin Han +1Time Series Generation Conditional Diffusion Models
Department of Applied Mathematics and Statistics, Data Science and AI Institute, and Mathematical Institute for Data Science, Johns Hopkins University. · Department of Industrial Engineering and Management Sciences, Northwestern University. · Department Management Science and Engineering, Stanford University.
Generating Financial Time Series by Matching Random Convolutional Features
Jun 3, 2026Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2Time Series Generation Random Feature Methods
Imperial College London · JPMorgan Chase & Co.‡
REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Jun 3, 2026Moulik Gupta, Dhruv Kumar, Murari Mandal +1Multivariate Time Series Forecasting Time Series Generation
Birla AI Labs, Office of Ananya Birla · Birla Institute of Technology and Science, Pilani · Kalinga Institute of Industrial Technology, Bhubaneswar
E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
Jun 1, 2026Lin Jiang, Dahai Yu, Ximiao Li +1Time Series Generation Generative Modeling
Florida State University
Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models
May 31, 2026Yunhao Zhang, Ruiying Qi, Jiale Zheng +3Zero-Shot Time Series Forecasting Time Series Generation
Shanghai Jiao Tong University · Huawei Noah’s Ark Lab
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
May 28, 2026Hwa Hui Tew, Junn Yong Loo, Fang Yu Leong +6Flow Matching Time Series Generation
School of Information Technology, Monash University Malaysia · Statistics Program, King Abdullah University of Science and Technology · School of Engineering, Monash University Malaysia
Universal Time Series Generation with Neural Controlled Differential Equations
May 27, 2026Torben Berndt, Elyes Farjallah, Leif Seute +3Neural Controlled Differential Equations Time Series Generation
Heidelberg Institute for Theoretical Studies, Heidelberg, Germany · IAR, Karlsruhe Institute of Technology, Karlsruhe, Germany · Max Planck Institute for Polymer Research, Mainz, Germany +4
High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework
May 26, 2026Giuseppe Masi, Andrea Coletta, Novella BartoliniSynthetic Data Generation Time Series Generation
Sapienza University of Rome, Italy · Banca d’Italia, Italy
Triangular-Reference Schrödinger Bridges for Time Series Generation
May 26, 2026Gabriele BocchiStochastic Differential Equations Schrödinger Bridges
Arakne S.r.l., Roma.
TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation
May 26, 2026Zesen Wang, Lijuan Lan, Yonggang Li +1Time Series Generation Time Series Data Augmentation
School of Automation, Central South University, Changsha, China
PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
May 22, 2026Junru Zhang, Lang Feng, Jinbo Wang +6Flow Matching Mixture of Experts
Zhejiang University, China · Nanyang Technological University, Singapore · I2R, Agency for Science, Technology and Research (A*STAR), Singapore
fMRI-Diffusion: Generating fMRI Time Series Via a Temporal Transformer Diffusion Model for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Depression Detection Time Series Generation
aSchool of Information and Communication Technology, Griffith University, Gold Coast, QLD, 4215, Australia
Let EEG Models Learn EEG
May 20, 2026Yifan Wang, Yijia Ma, Wen Li +1Electroencephalography Time Series Generation
Stony Brook University · University of Texas Health Center at Houston.

Aug 4, 2026J/K move · Enter open · S save

SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation

Lin Jiang, Dahai Yu, Ravikumar Gelli, Guang Wang

Department of Computer Science Florida State University Tallahassee, Florida, USA · Department of Electrical and Computer Engineering Florida State University Tallahassee, Florida, USA

Synthetic Data Generation Time Series Generation Synthetic Anomaly Generation Diffusion Models

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Fine-grained energy consumption data are essential for applications such as demand forecasting, demand response planning, and grid reliability assessment. However, access to such data is often restricted by privacy concerns and data-sharing constraints, motivating growing interest in synthetic energy data generation. Although existing methods can reproduce overall consumption distributions and recurring temporal patterns, they often smooth out or underrepresent anomalous events caused by extreme weather, infrastructure failures, and behavioral shifts. Preserving these events is challenging because they are sparse, localized in time and space, and shaped by heterogeneous dependencies across geographical proximity and regional attributes. To address these challenges, we propose SynEnergy, a two-stage diffusion-based framework for anomaly-preserving energy consumption data generation. The first stage, Heterogeneous Graph-based Anomaly Semantic Learning (HG-ASL), extracts region-specific anomaly semantics from sparse residual structures by jointly modeling spatial and attribute dependencies across urban regions. The second stage, Anomaly Semantic-guided Diffusion (AS-Diff), injects the learned anomaly semantics into the denoising process to generate realistic consumption sequences while preserving anomalous patterns. This design enables controllable generation for individual regions and scales naturally to city-wide settings. We evaluate SynEnergy on four real-world energy consumption datasets against 11 general-purpose and energy-specific generation baselines. Experimental results show that SynEnergy improves anomaly preservation fidelity by an average of 12.21% and downstream quality by 2.96%, while maintaining competitive overall generation fidelity compared to baselines.