Synthetic Time Series

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5 papers in the last 28 days · 0.1% of indexed attention

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Period ending 2026-09-21

2 new papers

A weekly snapshot of new work published in Synthetic Time Series.

Period ending 2026-09-14

3 new papers

A weekly snapshot of new work published in Synthetic Time Series.

55 papers

Latest in Synthetic Time Series

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ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation
Sep 21, 2026Subo Wei, Jianqi Gao, Mingyan Fan +3Synthetic Time Series
LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
Sep 14, 2026Mantek Singh, Jeshwanth Challagundla, Prateek Karnal +3Synthetic Time Series Manufacturing
CodeTS: Verifiable Text-to-Time Series Generation via Executable Code
Sep 14, 2026Xudong Yuan, Shunyu Liu, Tongya Zheng +3Synthetic Time Series Text Generation
LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
Sep 12, 2026Mariia Baranova, Adrien Petralia, Etienne Le Naour +3Demand Response Energy Resources
When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting
Sep 12, 2026Emma Andrews, Gianmarco MengaldoSynthetic Time Series Information-Theoretic Limits
MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis
Sep 7, 2026Yanhao Huang, Shibo Feng, Wanjin Feng +2Synthetic Time Series Medical Image Generation
Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness
Aug 12, 2026Haochen Zhang, Jiaheng Guo, Yu-Chao Huang +2Synthetic Time Series High-Fidelity Conditional Generation
DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces
Aug 12, 2026Hans Buehler, Blanka Horvath, Anastasis KratsiosDerivative Pricing Synthetic Time Series
GENADA: efficient generative time series adversarial attack framework
Aug 12, 2026Michael Baronov, Denis Vorobev, Margarita Rusanova +2Adversarial Examples Synthetic Time Series
Physics-informed Diffusion Generative Model for Time-Series Data Synthesis in Dynamic Systems
Aug 11, 2026Haiteng Wang, Yunfei Zhu, Tao Wang +4Synthetic Time Series Gas Turbine Combustor
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
Aug 11, 2026Luis Amorim, Vitor Cerqueira, Moises Santos +2Synthetic Time Series Anonymization
Evaluating Generative Time-Series Models on Data with Point Masses
Aug 10, 2026Jian XuSynthetic Time Series Determinantal Point Process
DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series
Jul 29, 2026Dennis Thumm, Billy Tim Anthony, Ying ChenCausal Inferences Synthetic Time Series
From Jumps to Signatures: a Generative Method for Temporal Point Processes
Jul 7, 2026Niels Cariou-Kotlarek, Vasileios LamposDeterminantal Point Process Synthetic Time Series
Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series
Jul 5, 2026Zitao Shuai, Zongzhe Xu, Yuntian Wu +3Synthetic Time Series Generative Models
Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins
Jul 1, 2026Md Rakibul Haque, Shireen Elhabian, Warren Woodrich PettinePhysiological Data Synthetic Time Series
Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss
Jun 30, 2026Mohamed Gueye, Yazid Attabi, Manuel Morales +1Synthetic Time Series Time Series
MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
Jun 27, 2026Andrea Carriero, Davide Pettenuzzo, Shubhranshu ShekharTime Series Foundation Models Synthetic Time Series
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
Jun 19, 2026Franco Martino O'Rourke, Ana Trisovic, Dimitris BertsimasTabular Prior-Data Fitted Network Synthetic Time Series
Informative Missingness to Generate Irregular Clinical Time Series
Jun 14, 2026Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora +4Missing Data Synthetic Time Series
PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation
Jun 13, 2026Emre Yusuf, Ren Takahashi, Jayabrata BhaduriSynthetic Time Series Generative Models
Towards a Unified Generative Model for Scarce Time Series with Domain Experts
Jun 13, 2026Zihao Yao, Qi Zheng, Jiankai Zuo +1Synthetic Time Series Generative Models
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Jun 9, 2026Du Yin, Hao Xue, Jinliang Deng +4Synthetic Time Series
Stochastic weather generators for high-frequency wind vector time series
Jun 8, 2026Mingshi Cui, Kevin Eng, Justin T. Greene +5Urban Wind Offshore Wind Turbine
Diffusion Models for Adaptive Sequential Data Generation
Jun 4, 2026Haoyang Cao, Minshuo Chen, Yinbin Han +1Synthetic Time Series Discrete Diffusion Model
Generating Financial Time Series by Matching Random Convolutional Features
Jun 3, 2026Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2Synthetic Time Series Time Series
REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Jun 3, 2026Moulik Gupta, Dhruv Kumar, Murari Mandal +1Synthetic Time Series Multivariate Time Series Forecasting
E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
Jun 1, 2026Lin Jiang, Dahai Yu, Ximiao Li +1Synthetic Time Series Discrete Diffusion Model
Universal Time Series Generation with Neural Controlled Differential Equations
May 27, 2026Torben Berndt, Elyes Farjallah, Leif Seute +3Synthetic Time Series Generative Models
Detecting Diffusion-Generated Time Series Under Generator Shift
May 27, 2026Zhi Wen Soi, Aditya Shankar, Gert Lek +4Synthetic Time Series Generators
FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
May 27, 2026Jun Bai, Ziyang Song, Yue LiElectronic Health Records Synthetic Time Series
High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework
May 26, 2026Giuseppe Masi, Andrea Coletta, Novella BartoliniSynthetic Time Series Synthetic Data
Triangular-Reference Schrödinger Bridges for Time Series Generation
May 26, 2026Gabriele BocchiSchrödinger Bridges Synthetic Time Series
TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation
May 26, 2026Zesen Wang, Lijuan Lan, Yonggang Li +1Synthetic Time Series Carbon Emissions
FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis
May 26, 2026Peiyu Duan, Jiyao Wang, Nicha C. Dvornek +4Brain Magnetic Resonance Imaging Synthetic Time Series
PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
May 22, 2026Junru Zhang, Lang Feng, Jinbo Wang +6Synthetic Time Series Multiscale Dynamics
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 +1Brain Magnetic Resonance Imaging Depression Detection
Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian
May 19, 2026Wenpin Tang, Nizar Touzi, Zikun Zhang +1Stochastic Differential Equations Diffusion Dynamics
DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
May 18, 2026Masahiro Suzuki, Bohui Xia, Hiroto Yamamoto +1Synthetic Time Series Time Series
GenTS: A Comprehensive Benchmark Library for Generative Time Series Models
May 18, 2026Chenxi Wang, Xiaorong Wang, Peiyang Li +1Synthetic Time Series Time Series
Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
May 13, 2026Elias Reich, Saverio Messineo, Stefan HuberGaussian Process Periodic Domains
Continuous Latent Contexts Enable Efficient Online Learning in Transformers
May 11, 2026Emile Anand, Abdullah Ateyeh, Xinyuan Cao +1In-Context Learning Transformer Architectures
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
May 7, 2026Hugo Cazaux, Eyjólfur Ingi Ásgeirsson, Hlynur StefánssonSynthetic Time Series Synthetic Data
Scalable inference of spatial regions and temporal signatures from time series
May 6, 2026Jiayu Weng, Alec KirkleySpatiotemporal Time Series
TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
May 2, 2026Seokhyun Lee, Jaeho Kim, Changjun Oh +2Synthetic Time Series Autoregressive Generation
SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
Apr 30, 2026Arnaud ZinflouMultivariate Time Series Imputation Electricity Price Forecasting
Preserving Temporal Dynamics in Time Series Generation
Apr 29, 2026Ci Lin, Futong Li, Tet Yeap +1Synthetic Time Series Generative Adversarial Network
Time Series Augmented Generation for Financial Applications
Apr 21, 2026Anton Kolonin, Alexey Glushchenko, Evgeny Bochkov +1Synthetic Time Series
Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling
Apr 14, 2026Pierre Lotte, André Péninou, Olivier TesteTime-Series Anomaly Detection Synthetic Time Series
Universal Redundancies in Time Series Foundation Models
Feb 2, 2026Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai +1Time Series Foundation Models Synthetic Time Series
Synthetic Time Series Generation via Complex Networks
Jan 30, 2026Jaime Vale, Vanessa Freitas Silva, Maria Eduarda Silva +1Synthetic Time Series Quantile Regression
Finite-Sample Unbiased Variance of MMD under Unbalanced Sampling: Exact Estimation and Quasi-Linear Computation
Jan 20, 2026Shijie Zhong, Yikun Yang, Da Gong +1Maximum Mean Discrepancy Finite-Sample
Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
Jan 20, 2026Xu Zhang, Junwei Deng, Chang Xu +2Synthetic Time Series Neural Ordinary Differential Equations
L-GTA: Latent Generative Modeling for Time Series Augmentation
Jul 31, 2025Luis Roque, Vitor Cerqueira, Carlos Soares +1Synthetic Data Augmentation Synthetic Time Series
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Synthetic Time Series Stochastic Systems

Sep 21, 2026 · cs.LGJ/K move · Enter open · S save

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

Subo Wei, Jianqi Gao, Mingyan Fan, Shaorong Xie+2

Text-controlled time series generation aims to synthesize sequences that follow natural-language descriptions while remaining faithful to real data distributions. Existing paradigms often couple semantic understanding and sequence modeling in a single continuous latent space, lacking explicit local semantic anchors and separation between global continuous attributes and local discrete shapes. As a result, key local structures may be smoothed, missed, or misplaced. We propose Shape Lexicon (ShapeLex), which decouples text-to-sequence generation into discrete symbolization of local shapes and continuous modeling of global attributes. ShapeLex first induces a reusable vocabulary of discrete shape units, such as rises, spikes, and sharp drops, from training data, forming an interpretable symbolic space. An autoregressive generator then selects shapes according to the textual description, adjusts attributes such as position and duration, and composes them in temporal order into a shape skeleton. Finally, a mixture-density scale head models and samples the overall level and volatility to restore realistic global scale. Experiments on twelve public datasets, real user-written text, and downstream forecasting tasks show that ShapeLex generates series that better match real data distributions than existing methods. In addition, paired supervision is automatically synthesized from the learned vocabulary, avoiding annotation costs that grow with dataset size and improving scalability.