Time Series Data Augmentation

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 19

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Image Synthesis as an Intermediate for Controllable Time Series Generation
Oct 4, 2026Haochen Yuan, Jing Xie, Yunbo WangTime Series Forecasting Time Series Generation
MoE Key Lab of Artificial Intelligence, Institute of AI, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China
WinoTS: Wavelet-based Self-Distillation for Time Series Models
Sep 30, 2026Noam Major, Kathy Razmadze, Yoli ShavitSelf-Supervised Time Series Representation Learning Time Series Data Augmentation
Bar-Ilan University, Ramat-Gan, Israel
Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling
Sep 14, 2026Rafael Pina, Varuna De Silva, Mindula IlleperumaSynthetic Data Augmentation Time Series Generation
Institute for Digital Technologies Loughborough University London London, United Kingdom
DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning
Sep 14, 2026Wenrui Xu, Anas Enanaa, Keshab K. ParhiEEG Seizure Detection Representation Learning
University of Minnesota, Twin Cities
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
Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning
Jun 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorContrastive Learning Time Series Classification
Norwegian University of Science and Technology, Norway · United States Naval Academy, USA
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
Jun 14, 2026Tien Thanh ThachTime Series Forecasting Time Series Data Augmentation
Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
A Systematic Approach for Selecting Trajectories for Data Augmentation
Jun 9, 2026Adam NordlingSynthetic Data Augmentation Data Augmentation
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
A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection
Jun 9, 2026Davood Fattahi, Runze Yan, Saurabh Kataria +2Time Series Data Augmentation Photoplethysmography
Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA · Department of Computer Science, Emory University, Atlanta, GA, USA · Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA +1
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
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
DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
May 18, 2026Masahiro Suzuki, Bohui Xia, Hiroto Yamamoto +1Time Series Forecasting Time Series Data Augmentation
Sony Group Corporation Tokyo, Japan
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
May 7, 2026Hugo Cazaux, Eyjólfur Ingi Ásgeirsson, Hlynur StefánssonSynthetic Data Augmentation Synthetic Data Generation
Department of Engineering Reykjavík University Iceland, Menntavegur 1, 102
Preserving Temporal Dynamics in Time Series Generation
Apr 29, 2026Ci Lin, Futong Li, Tet Yeap +1Time Series Generation Time Series Data Augmentation
IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation
Apr 22, 2026Mingchun Sun, Rongqiang Zhao, Muhammad Abdul Munnaf +1Synthetic Data Augmentation Data Augmentation
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China · State Key Laboratory of Smart Farm Technologies and Systems, Harbin 150001, China · Agricultural Biosystems Engineering, Wageningen University and Research, Wageningen 6703 CH, Netherlands
CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction
Apr 16, 2026Honglin Guo, Rihao Chang, He Jiao +3Clinical Prediction Time Series Data Augmentation
Tianjin University, Tianjin 300072, China · Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China · Tianjin Medical University General Hospital, Tianjin 300052, China
Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting
Apr 10, 2026Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr +1Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Data Science Group University of Hildesheim Hildesheim, Germany · Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Hildesheim, Germany
L-GTA: Latent Generative Modeling for Time Series Augmentation
Jul 31, 2025Luis Roque, Vitor Cerqueira, Carlos Soares +1Variational Autoencoders Synthetic Data Augmentation
Faculdade de Engenharia da Universidade do Porto, Porto, Portugal · University of Coimbra, Coimbra, Portugal · Fraunhofer Portugal AICOS, Portugal +1

Oct 4, 2026, cs.AIJ/K move · Enter open · S save

Image Synthesis as an Intermediate for Controllable Time Series Generation

Haochen Yuan, Jing Xie, Yunbo Wang

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

Time Series Forecasting Time Series Generation Time Series Data Augmentation

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Semantic-driven time-series generation offers a promising way to improve downstream learning in few-shot forecasting, but directly generating numerical sequences from language often fails to preserve the intended temporal structure. We propose VisualBridge, which uses time-series plots as a visual intermediate to bridge high-level temporal semantics and numerical sequences. An MLLM first converts plotted series into structured semantic representations, enabling explicit control over temporal properties such as trend, seasonality, and volatility. We then learn a semantic editing policy with downstream forecasting rewards, allowing the generation process to favor temporal patterns that are beneficial for the target task. The resulting sequences are further modeled by a temporal VAE to produce consistent multivariate augmentations. Experiments on standard public forecasting benchmarks demonstrate that VisualBridge improves few-shot forecasting over conventional augmentation methods, with ablations validating the roles of visual semantic grounding, learned semantic control, and VAE-based generation.

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