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.

Nov 6, 2026

Grid-Oriented Probabilistic Load Forecasting for AI Data Centers

Probabilistic load forecasts for AI data centers need to represent both uncertainty under changing workloads and temporal dependence to support grid operations. This paper develops an open, grid-oriented benchmark using measured GPU power from a 448-GPU AI cluster. Forecasts are evaluated through four stages: probabilistic prediction, online uncertainty calibration, temporal scenario generation, and horizon-wide reserve sizing. Following a change in cluster utilization, a quantile LightGBM forecast achieves only 63.4% coverage for a nominal 90% interval, while online regime-conditioned calibration increases coverage to 87.3%. Even with well-calibrated marginal forecasts, independent sampling produces unrealistic ramp distributions and excessive reserve procurement. Reconstructing temporal dependence from historical forecast errors reduces the discrepancy in ramp statistics and lowers realized reserve cost by 7.4-10.6% across three forecasting tasks. These findings show that marginal forecast quality alone is insufficient for operational decisions and motivate joint evaluation of calibration, temporal structure, and decision outcomes.

Qingyi Yao, Ding Lin, Tianqiao Zhao +2

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

Electricity Load ForecastingProbabilistic Time Series ForecastingForecasting BenchmarksUncertainty CalibrationData Center Energy ManagementTime Series Generation
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Nov 6, 2026

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

Real-world multivariate time series rarely come with verified ground-truth causal structure, so research on time-series causal discovery and treatment-effect estimation depends on synthetic data. Existing tools are either fixed datasets that cannot be modified after release or script-based generators in which each script fixes one combination of mechanism, confounding, and missingness. Neither supports controlled experiments that vary a single property, such as sparsity, noise distribution, or missingness mechanism, while holding the others fixed. Counterfactual evaluation has the same gap, since existing simulators are built per paper with hardcoded mechanisms or produce observational data only. We introduce DTCD (Datasets and Toolkit for Causal Discovery), a data generation library that produces multivariate time series with ground-truth contemporaneous and lagged adjacency matrices. Topology, mechanism, noise, observational mask, and intervention policy are independent components that compose through configuration alone. Paired counterfactual twins, produced by caching the stochastic realization during the factual rollout and re-injecting it during the interventional rollout, recover individual treatment effects to numerical precision. A reference benchmark of eight discovery algorithms on eighty-one configurations and four treatment-effect estimators on eighteen configurations exposes failure modes that aggregate metrics hide. These include the inability of standard discovery methods to handle missing values, regardless of whether the missingness mechanism is informative, and a marginal-variance shortcut (varsortability) that we measure and remove by standardization. Code and data: https://github.com/hferdous/Composable_Causality

Muhammad Hasan Ferdous, Md Osman Gani

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

Time-Series Causal DiscoveryTime Series GenerationSynthetic Data EvaluationIndividualized Treatment Effect Estimation
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Nov 3, 2026

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

Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without increasing generative complexity. We propose UniFlow, a unified framework that integrates multi-scale representation learning with efficient non-autoregressive generation through a shared-semantic discrete interface. Specifically, a multi-scale tokenizer employs shared-index voting to consolidate complementary temporal information into a single public-ID sequence, while scale-specific codebooks preserve the information required for reconstruction. Building on this unified representation, we develop geometry-aware categorical flow matching, which initializes transport from learned low-rank anchors and generates public identities non-autoregressively. This design captures multi-scale temporal structure without requiring multiple generative token streams or sequential token decoding. Experiments across four benchmarks and both short- and long-term settings demonstrate improved generative fidelity, strong cross-scale consistency, and substantial inference speedups over representative autoregressive and diffusion-based baselines. Code is available at https://github.com/William-Liwei/uniflow

Shibo Feng, Wei Li, Min Wu +2

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

Time Series GenerationFlow-Based Generative ModelingFlow MatchingDiscrete Flow Matching
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Nov 3, 2026

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

What makes a pretraining corpus transferable? We describe a corpus by three properties: its structure, its dimensionality, and the distribution shape of each unit's values. We propose the Transfer-Invariance Principle: the same property can be irrelevant to transfer under one input transform and decisive under another, so the model's input transform, not the data modality, decides which properties can matter. To test it we build TransferForge, a generator that places a synthetic population at any prescribed structure, dimensionality, and distribution shape. KernelSynth, the synthetic prior of Chronos, cannot, because it draws independent series. When the model standardizes each input window (PatchTST on UCR), the distribution shape is inert and the structural setting governs. Synthetic pretraining then matches a real corpus of the same size and exceeds it once scaled. When the model encodes calcium fluorescence signals without normalization (POYO Plus), the shape becomes a governing constraint, and a shape-matched synthetic corpus replaces real pretraining with zero real neural activity. Flipping only the input normalization, with the model and data fixed, changes the effect of a shape manipulation by a factor of 5.3, so the pipeline, not the modality, sets the switch. The principle yields a recipe that chooses what to synthesize before any training. Pretraining data then becomes something to manufacture for the model, most valuably where real data is scarce, expensive, or private.

Sangyoon Bae, Jiook Cha

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

Transfer LearningSynthetic Data PretrainingTime Series Generation
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Nov 2, 2026

SynForecast: Controlled Synthetic Time Series for Forecasting Research

Synthetic time series constitute a substantial share of the training corpora of several recent forecasting foundation models. Existing studies report benefits at small data budgets and modest synthetic proportions, but each evaluates individual combinations of model and generator. Studying these benefits systematically requires generators that support controlled configuration, perturbation, reproducibility and mechanism-level ablation, and the 11 existing generators we review show gaps in this combined support. We present SynForecast, an open-source Python library with 32 generators, 19 of which implement named mechanisms with a published source in econometrics, quantitative finance, statistical physics and point-process theory. Perturbations and mechanism families can be varied while everything else is held fixed, output comes in the long format that forecasting libraries accept, and output does not depend on the number of parallel workers. We evaluate SynForecast on M4 at five frequencies with a neural network and a gradient-boosted ensemble. A classical baseline attains the lowest error in nine of ten frequency-history combinations. A synthetic prior outperforms the same network trained from scratch at a fixed budget by up to 34% with a few dozen observations, but loses to it at full history at every frequency, and none of three pre-specified rescalings of history predicts where the sign changes. At the hourly frequency, where the prior fails at every history, the corpus lacks daily cycles; adding them reduces zero-shot error by 43 to 63% for both models. Fine-tuning outperforms training from scratch by up to about 15%, but at short histories a fixed 300-step fine-tune is worse than the pretrained model used as is. Removing the classical mechanism family costs about 2% for both models; every other family has a smaller effect outside the hourly frequency.

Olivier Sprangers, Cristian Challu, Jan Rathfelder +2

Nixtla, San Francisco, CA, United States

Time Series GenerationSynthetic Data PretrainingTime Series ForecastingTime-Series Foundation ModelsForecasting Benchmarks
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Nov 1, 2026

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

This paper presents a multimodal generative model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data and improve next event prediction. The framework employs a Transformer Hawkes Process (THP) to encode event data and an iTransformer to encode time-series data, integrated via a co-attention mechanism that effectively captures cross-modal dependencies between discrete events and continuous signals. From these integrated representations, we employ two specialized decoders for simulation: a Conditional Latent Diffusion Model (CLDM) for modeling future sensor trajectories and the Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process (SMURF-THP) for high-fidelity event prediction. This dual-decoder strategy supports uncertainty-aware, history-conditioned simulation by rolling the model forward for a specified number of generation iterations, producing finite stochastic scenarios rather than deterministic extrapolations over the full observational record. Evaluated on a comprehensive dataset from the Red Rock Solar Site in Arizona, our approach improves aggregate event-prediction F1 relative to baselines and supports applications in predictive maintenance, fault detection, and energy optimization for solar power systems.

Jiayu Huang, Boyang Xu, Yongming Liu +1

Arizona State University, Tempe, AZ, USA

Time Series GenerationPredictive MaintenanceConditional Generative ModelingUncertainty-Aware Multimodal FusionTemporal Point Processes
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Oct 31, 2026

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

Across heterogeneous real-world domains, time-series generation supports simulation, model development, and data augmentation by generating sequences that preserve target-domain statistics and temporal dependencies when real observations are scarce or costly. However, existing methods predominantly address in-domain generation, limiting their reuse in unseen domains. We study reference-conditioned zero-shot time series generation: target domains are unseen during pretraining, generation uses a single target-domain reference sequence, and no model parameters are updated. MarkovTS characterizes sequence evolution through local morphologies and their ordered transitions at multiple temporal scales. Its pretrained encoder quantizes normalized time-series patches into multiscale morphological states with a state codebook, while a transition codebook encodes contextual transitions between adjacent intervals. These transferable state-transition representations condition a pretrained flow-matching model. At generation, the frozen model combines a reference-derived condition with independent Gaussian noise to produce stochastic samples that retain the reference's temporal organization. Across 12 unseen target datasets, MarkovTS outperforms strong baselines in most distributional comparisons and generates diverse, reference-consistent sequences. As downstream augmentation, its samples yield overall gains across forecasting, classification, and anomaly detection benchmarks and diverse model families. Our code is available at https://github.com/blisky-li/MarkovTS.

Yujie Li, Zezhi Shao, Jingbo Zhou +3

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

Time Series GenerationConditional Generative ModelingTime Series Representation Learning
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Oct 30, 2026

Conditional Neural SDEs for Financial Time-Series Generation

Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external context. It has direct applications in finance and economics. Neural stochastic differential equations provide a natural framework for this task by combining continuous-time stochastic dynamics with learnable drift and diffusion functions. However, directly modeling the target state can be unstable in conditional settings, especially when the target and conditioning variables have strong level dependence, limited training data, or different dimensions.We propose a residualanchor Neural SDE architecture that decomposes the target into a learned conditional anchor and a stochastic residual. The anchor captures the context-dependent baseline, while the Neural SDE (Stochastic Differential Equation) models the residual dynamics around this baseline. This separation reduces the burden on the stochastic dynamics and generalizes spread-based representations to more general conditioning settings.We evaluate the approach on real-world financial time series, including conditional interest-rate scenarios and a comparison with a time-series foundation model. The results show that the proposed model can generate plausible conditional paths, maintain competitive point accuracy, and provide calibrated uncertainty estimates.

Sina Molavipour, Alireza M. Javid, Björn Löfdahl +1

Time Series GenerationStochastic Differential EquationsFinancial Time Series
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Oct 30, 2026

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

Historically rare ''one-in-a-generation'' storms are occurring with unprecedented frequency, causing catastrophic damage and mounting casualties, and exposing the vulnerability of infrastructure designed around short observational records. To overcome the limitations of sparse data, we present StormGen, a generative machine-learning model that transforms an observed storm's initial hours into complete, physically plausible hourly rainfall lifecycles in minutes. Evaluated on a decade of southeast Texas rainfall, StormGen produced close counterparts for 4\5% of the most severe 2% of storms--compared to just 3% for stochastic storm transposition--and maintained spatial coherence over full 24-hour sequences. Crucially, from a single hour of initial rainfall, the model successfully reconstructed the specific lifecycle, spatial pattern, and extreme catchment totals of Hurricane Harvey itself, despite the event being entirely absent in training. Because storm timing alone can more than double peak flows, these complete generated scenarios provide a vital mechanism to stress-test flood protections against unprecedented extremes.

Valerie Tsao, Nathaniel W. Chaney, Manolis Veveakis +1

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

Time Series GenerationSpatiotemporal ForecastingExtreme Weather ForecastingConditional Generative Modeling
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Oct 25, 2026

Conditional Diffusion Generation of DVL and Inertial Signals for Underwater Navigation

Autonomous underwater vehicles (AUVs) navigate without satellite positioning and rely on Doppler velocity log (DVL) and inertial sensors for motion estimation. Deep learning methods for processing these sensors require training data covering the motion the AUV encounters, commonly obtained through costly sea trials. To overcome this limitation, we present a conditional diffusion framework that generates synthetic DVL and inertial signals to replace real recordings in training. The generator employs an Elucidated diffusion model conditioned on three per-channel motion descriptors. Three configurations are evaluated: DVL beams alone, inertial channels alone, and all channels jointly from one model. Across thirteen trajectories of the real dataset, the generated signals followed the requested conditions with Pearson correlations between 0.72 and 0.997, transferred to trajectories withheld from generator training, and did not replicate any training window. A BeamsNet velocity estimator trained on generated signals alone, with no real recording retained, matched the velocity error obtained with the full real training set. Replacing up to all real windows with generated ones caused no measurable loss. Thus, this approach reduces the dependence of learned AUV navigation on sea-trial data, enables training when recordings are scarce, and facilitates developing and evaluating deep learning methods for underwater navigation.

Michal Levin, Itzik Klein

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

Synthetic Data GenerationAutonomous Underwater NavigationUnderwater RoboticsTime Series GenerationDiffusion Models
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Oct 19, 2026

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

Deep generative models provide an effective modern solution to alleviating the problem of data scarcity by synthesizing realistic time series. However, despite the considerable progress achieved by existing deep time series generators, two key challenges remain: (1) learning hierarchical representations for heterogeneous temporal patterns; and (2) coordinating the generation process of heterogeneous temporal dynamics. To address these key bottlenecks, in this work, we propose ORBIT, a unified deep generative framework for time series. Specifically, we develop two key components: the Frequency-Hierarchical VQ-VAE (FH-VQ-VAE) and the Frequency-Band Autoregressive Transformer (FBAR). The former learns hierarchical representations by decomposing latent temporal representations into different frequency bands and applying residual quantization with increasing token budgets. The latter establishes a novel low-to-high frequency autoregressive direction, yielding a time series generation process that first constructs global temporal structures and then incrementally incorporates fine-grained temporal details. These compact designs not only disentangle heterogeneous temporal patterns for more effective representation learning but also mitigate potential interference among different temporal dynamics during generation. Extensive experiments on multiple challenging real-world benchmarks demonstrate that ORBIT achieves state-of-the-art overall performance while exhibiting superior inference efficiency compared with advanced baselines.

Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +5

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

Time Series GenerationFrequency-Domain Time Series ModelingAutoregressive TransformerTime Series Representation LearningVector-Quantized Variational Autoencoder
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Oct 18, 2026

FracTS: Hierarchical and Autoregressive Time Series Generation

Time series are ubiquitous across different domains, and the growing use of synthetic data for privacy-preserving sharing, data augmentation, and stress testing makes time series generation increasingly important. Early generative approaches are predominantly autoregressive, which can suffer from low sample diversity, error accumulation, and difficulty modeling long-range dependencies. In this paper, we introduce FracTS, a hierarchical, coarse-to-fine autoregressive model for multivariate time series generation. Inspired by fractal generative modeling developed for images, FracTS adapts the paradigm from 2D to 1D sequences by learning a multi-scale representation that captures global trajectory structure at coarse scales and local dynamics at fine scales while following the autoregressive nature of time series. In contrast to unstructured images, time series are structured, allowing easy automated or designated temporal feature processing optimized for the model, and global (static) feature extraction describing the instances that generate the corresponding time series in the collection. We incorporate them in the generation process to further improve the generation quality. The design naturally handles multivariate dependencies and improves long-horizon temporal coherence without requiring fixed-length inputs. Experiments on five datasets (two conditional, three unconditional) conducted against seven baselines show that FracTS consistently outperforms baselines across marginal distribution and predictive ML utility, and correlation preservation in the unconditional setting. In particular, the predictive ML utility is averagely improved by 100.0% and 75.0-81.0% for unconditional and conditional time series datasets compared to the best baseline (relative error computed on dataset-averaged raw values). Code is available at: https://github.com/BetterdataLabs/fracts.

Jiayu Li, Umair Afzal, Zilong Zhao +3

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

Time Series GenerationAutoregressive GenerationMultiscale Time-Series ModelingConditional Generative Modeling
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Oct 12, 2026

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

Generating realistic time-series requires faithfully reproducing rare but high-impact extreme events. Existing approaches often rely on specialized representations or extreme-focused training objectives, which may restrict the diversity of captured extremes or compromise overall distributional fidelity. We propose Cross-View Time-Frequency Diffusion (CrossTF), a framework that exploits discrepancies between time- and frequency-view denoisers for extreme-aware time-series generation. Our key empirical finding is extreme-sensitive cross-view asymmetry: moving along the direction connecting the two views' clean-sample estimates substantially changes extreme fidelity while having limited effects on overall generation quality. We further identify a late-stage cross-view window in which this discrepancy stabilizes and cross-view adjustments have a particularly strong effect on extreme fidelity. These findings motivate a staged optimization strategy in CrossTF: after training and freezing both denoisers, it only needs to learn step-specific fusion parameters within this window using an Extreme Value Theory-based objective under an energy-distance constraint. This formulation supports interpolation between and extrapolation beyond the view-specific estimates while limiting degradation in overall generation quality. Extensive experiments on seven real-world datasets against 11 baselines show that CrossTF improves extreme fidelity by 26.1% and overall performance by 7.4% relative to the strongest baselines. These results highlight cross-view discrepancy as an effective basis for low-dimensional optimization in extreme-aware time-series generation.

Lin Jiang, Rongchao Xu, Guang Wang

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

Time-Frequency AnalysisTime Series GenerationDiffusion Models
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Oct 12, 2026

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

We introduce IND-Eval, a public benchmark that organizes industrial time series across domains into source-specific forecasting tasks. Relative to established benchmarks such as GIFT-Eval, it emphasizes finer sampling resolutions and a greater prevalence of multivariate forecasting. Evaluating Chronos-2, TiRex-2, and TimesFM-3, we find that TimesFM-3 achieves the lowest source-balanced aggregate nMASE and nCRPS and leads seven of nine sources under each metric; source-specific exceptions reinforce the need for domain-representative evaluation for model selection. These findings also motivate improving domain alignment during pretraining, where real industrial data are often too scarce to close domain-specific gaps directly; we therefore investigate whether a domain's statistical structure can guide simulator design. In a case study on two offshore well-monitoring regimes, we improve a general simulator's coverage using an oscillator-augmented simulation pipeline. This lowers forecasting error in the target domain but reduces performance on the subset of GIFT-Eval with natively univariate targets. A 50/50 mixture of the general and augmented simulation recipes achieves the lowest error among the three training recipes for all four event-metric pairs in the case study while recovering most of the lost GIFT-Eval performance. Together, these results illustrate how domain-representative evaluation can guide model selection and the composition of simulated data while revealing a trade-off between specialization and generality.

Vebjørn Haugland, Pavel Gulin, David Foss +5

Cognite

Time Series ForecastingTime-Series Foundation ModelsForecasting BenchmarksMultivariate Time Series ForecastingTime Series Generation
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Oct 10, 2026

MontageFlow: Physically Valid High Fidelity EEG Generation

Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints. Although recent generative models report substantial improvements in generation fidelity, we show that these gains conceal a categorical failure. EEG is not an arbitrary multichannel signal: each channel records the voltage difference between two scalp electrodes. So, real recordings satisfy exact linear identities among their channels. Current generators invariably violate these identities, yet this has gone unnoticed. We therefore propose MontageFlow, a flow-matching model that enforces these identities by restricting its output to signals that satisfy them. We find that ensuring validity improves fidelity too. Furthermore, to retain the EEG characteristics that oversmoothing washes out, we also condition the model on a physiological state vector encoded from the whole EEG window. For generation, this vector is drawn from a prior fitted in latent space. Across 3 large-scale benchmarks, our model improves TS-FID by 1.6-3.6×\times over the closest rival while ensuring validity.

Sudarshan Regmi, Nabaraj Subedi, Arvind Pillai +6

Dartmouth College

EEG Signal GenerationTime Series GenerationConstrained Generative ModelingFlow-Based Generative ModelingPhysics-Informed Generative ModelingConditional Generative Modeling
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Oct 7, 2026

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

Differentially private time-series generators commonly produce fixed-length synthetic windows, whereas downstream forecasting models often require long continuous training sequences. How these windows are assembled after generation can therefore alter the effective synthetic data presented to a forecaster, even when the trained generator remains unchanged. We study this post-generation sequence assembly process by systematically varying overlap rates and window-weighting schemes and evaluating the resulting sequences in terms of boundary continuity, statistical and temporal fidelity, and Train-on-Synthetic-Test-on-Real (TSTR) forecasting utility. Across four types of public datasets (ETTh1, ETTm1, Weather, and Appliances) and five forecasting models, the results reveal a clear forecaster-dependent assembly principle: downstream TSTR utility is jointly shaped by the forecaster, overlap rate, and window-weighting scheme, leading to distinct assembly preferences across forecasting models. Increased overlap generally improves boundary continuity, but improvements in continuity or individual fidelity diagnostics do not consistently reduce forecasting error, indicating that these diagnostics alone are insufficient for selecting assembly configurations. Complete five-forecaster assembly grids, together with matched Train-on-Real-Test-on-Real (TRTR) references, further characterize these regularities and quantify assembly-dependent utility relative to real-data training. We then validate the identified principles through additional analyses of robustness and generator variability.

Guoxiong Long, Huizhen Huang, Qikun Cai +2

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

Synthetic Data EvaluationSynthetic Data GenerationTime Series GenerationDifferential PrivacyTime Series Forecasting
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Oct 6, 2026

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

Flow-matching models start from an isotropic Gaussian source, the standard choice when the correlation structure of the data is unknown in advance. For multi-channel brain recordings, however, part of this structure is known in advance. Electrodes sit at fixed positions on the head, and volume conduction through the skull and scalp makes nearby electrodes co-vary in a way that is shared across subjects. Existing EEG generative models nonetheless leave the network to learn this from scratch. We put this structure into the source instead. From the sensor coordinates alone, we build a k-nearest-neighbor graph and take a graph-Matérn function of its Laplacian as the source covariance, so the flow starts from spatially coherent patterns rather than channel-independent noise. The change adds no learned parameters, works with any coupling and any drift network, and uses the same three hyperparameters on every dataset. Across eight EEG datasets and four flow-matching methods, the graph-Matérn source lowers the spectral discrepancy between generated and real signals in the five clinical bands (PSD-KL) on most datasets. PSD-KL falls by 12% to 17% in geometric mean over datasets depending on the method and by up to 40% on PhysioNet-MI, the densest montage. We show that the improvement stems from the spatial eigenvectors of the local graph of sensor positions, since randomizing the eigenvectors while preserving the eigenvalue spectrum eliminates the gain. Furthermore, a prior fitted directly to the empirical data covariance performs worse than isotropic noise. The same construction applies unchanged to MEG, intracranial EEG with patient-specific grids, and a traffic-sensor network, lowering PSD-KL for every method on each. https://jd730.github.io/projects/GraphPrior

Jaedong Hwang

Massachusetts Institute of Technology

ElectroencephalographyEEG Signal GenerationTime Series GenerationFlow-Based Generative Modeling
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Oct 5, 2026

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

Synthetic patient data generation is a promising solution to the dual challenge of data scarcity and privacy constraints in healthcare machine learning. Realistic synthesis of patient-level clinical data requires jointly modeling heterogeneous static covariates, irregularly sampled longitudinal trajectories, and informative observation times - three tightly coupled components in practice yet rarely addressed together. We propose HyperNSDE, a continuous-time generative model that conditions a latent Neural SDE on static patient representations through a hypernetwork, allowing baseline characteristics to shape trajectory evolution beyond the initial condition without requiring a trajectory encoder, while stochastic latent dynamics capture realistic variability in generated paths. Observation times are modeled jointly through a latent-state-dependent intensity process, and training on irregular stochastic paths is stabilized via a deterministic-stochastic path decomposition with a non-adversarial signature-kernel objective. Experiments on simulated and real clinical datasets show improved observation-time fidelity and competitive performance, while matched-grid analyses reveal that forecasting and correlation metrics are affected by observation-grid regularity and trajectory smoothness.

Perrine Chassat, Agathe Guilloux

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

Stochastic Differential EquationsSynthetic Data GenerationTime Series GenerationSynthetic Clinical Data GenerationLongitudinal EHR ModelingConditional Generative Modeling
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Oct 4, 2026

Image Synthesis as an Intermediate for Controllable Time Series Generation

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.

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 ForecastingTime Series GenerationTime Series Data Augmentation
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Sep 30, 2026

Wavelet Flow Matching for Time Series

Synthetic time series are increasingly used for data augmentation, privacy-preserving data sharing, and downstream model development, yet faithfully reproducing both multi-scale temporal structure and cross-channel dependencies remains challenging. We study multivariate time-series generation through flow matching in the wavelet domain. By operating on multilevel discrete wavelet coefficients rather than directly in the time domain, the model represents coarse structure and progressively finer details at separate scales. Their naturally different variances further induce an implicit coarse-to-fine generative process without requiring an explicit multi-scale schedule. Since the transform acts independently on each channel, we pair it with a channel-token transformer whose attention directly models cross-channel dependencies. Across seven benchmark datasets and four sequence lengths, our method is best or tied on a majority of dataset-metric combinations, with the largest and most consistent improvements in Context-FID and discriminative score.

Lucas Poinsignon, Jorge da Silva Goncalves, Samuel Ruiperez-Campillo +1

Dept. of Computer Science ETH Zurich, Switzerland

Flow MatchingTime Series GenerationFlow-Based Generative Modeling
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Sep 29, 2026

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

Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, this paradigm faces three challenges: (1) a new training run is required for every dataset; (2) different data modalities, such as single tables, time series, and relational databases, require task-specific models and feature engineering; and (3) the resulting model is opaque, making its behavior under data constraints difficult to inspect. We propose GENSCRIPT, an inference-only pipeline that eliminates model training. GENSCRIPT computes a deterministic statistical profile of the source data (column types, ranges, missingness, categories, correlations, etc.) and passes it--rather than raw rows--to a language model to infer field semantics and cross-column integrity constraints. A coding agent then compiles the profile and constraints into an executable, auditable sampler. This unified approach supports single-table, temporal, and relational data without task-specific modeling. Across four single-table benchmarks, GENSCRIPT builds generators in 2 minutes and samples 50k rows within 6 seconds, while remaining within a few points of leading methods in marginal fidelity. Notably, it is the only method that perfectly preserves a 1-to-1 mapping between columns in the Adult dataset. On a smart-building dataset, it produces conditional time series that more closely match the real distribution than two baselines and perfectly preserves primary- and foreign-key relationships in the corresponding relational database.

Zilong Zhao, Abdul Raheem, Jiayu Li +5

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

Constrained Generative ModelingSynthetic Data GenerationTime Series GenerationLLM-Based Program SynthesisSynthetic Tabular Data Generation
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Sep 28, 2026

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

Most existing time series generators rely on a two-stage modeling paradigm: the first stage learns discrete latent representations of time series; the second stage performs autoregressive modeling on these discrete latents through next token prediction. However, this paradigm suffers from two stage-specific limitations: the first stage can lead to information loss when discretizing continuous time series, while the second stage is prone to error accumulation during autoregressive generation. To address these limitations, our core idea is to perform generative modeling in a continuous latent space with a more efficient autoregressive framework. We propose HALO, which enhances time series generation via Hyperspherical Latents and Masked Autoregressive modeling to achieve this goal by tackling two key bottlenecks: (1) variance and scale heterogeneity of continuous latent representations; (2) the difficulty of balancing generation efficiency with temporal correlation modeling. HALO first introduces a hyperspherical VAE that constrains continuous latents to a fixed-radius hyperspherical shell, effectively stabilizing the numerical fluctuations of continuous latent representations. Secondly, we develop a masked autoregressive model that balances parallel decoding and temporal correlation learning, substantially reducing the number of inference steps required for generation and improving generation stability. Our extensive experiments demonstrate that HALO achieves state-of-the-art generation performance while offering significantly improved inference efficiency over existing advanced baselines.

Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +2

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

Variational AutoencodersTime Series GenerationAutoregressive GenerationAutoregressive Modeling
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Sep 27, 2026

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

Generative models for multivariate time series are routinely evaluated with pooled distributional metrics computed after marginal calibration. We show that, under a held-out protocol, the calibrated Kolmogorov-Smirnov (KS) distance differs from a real-versus-real floor by at most the distance of the calibrated sample to the training block, so that it carries little information about the generator. We audit a three-discriminator GAN with evolutionary weight adaptation (StatD2GAN) against re-implemented RCGAN and TimeGAN baselines on ERA5 weather sequences under a pre-specified protocol: the final two calendar years are held out behind a 168 hour embargo, calibration is fitted on the training block only, and the decision rules were frozen in advance. Evidence comes from 25 matched (location, seed) pairs across five locations, tested with Holm-corrected Wilcoxon signed-rank tests. First, isotonic calibration drives KS to a median of 1.019 times a per-location noise-and-shift floor, and neither baseline is distinguishable from StatD2GAN on it. Second, the sorted-representation discriminator is the only component whose removal significantly degrades cross-variable dependence (Kendall tau MAE +0.116, Holm p = 0.002); a rank-transformed variant also degrades it, consistent with quantile supervision rather than copula matching. Third, calibration raises physical constraint violations; projection removes them, but the largest marginal change (0.0110, Lhasa) exceeds the pre-specified tolerance in three locations. Fourth, the re-implemented TimeGAN is significantly better than StatD2GAN on Kendall tau and ACF MAE, RCGAN is worse on Kendall tau MAE and constraint violations, and the pre-specified collapse criterion was not met. We recommend floor-referenced marginal evaluation, matched-pair testing, and dependence and sequence-level metrics.

Mustafa Özaytaç, Özge Karadağ Ataş

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

Time Series GenerationModel CalibrationGenerative Adversarial NetworksSynthetic Data Evaluation
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Sep 27, 2026

ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation

Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynamics under a given sampling structure. This makes them illsuited for generating irregular time series in their native form, where a model must capture not only feature values, but also how many observations occur, when they occur, and which features are observed together. To address this heterogeneous generation problem, we propose ChronoFlow, a unified hierarchical flow matching framework organized by statistical granularity. Following a coarse-to-fine hierarchy, ChronoFlow first generates observation counts and feature-wise frequencies, then jointly generates observation times and feature co-observation patterns, and finally generates values conditioned on the realized pattern. This turns a complex joint generation problem into structurally aligned subproblems while preserving their dependencies. To evaluate complete irregular time series generation, we introduce complementary metrics spanning sample realism, sampling structure, value fidelity, and temporal and cross-feature dependencies, and validate them through controlled corruptions. Across five benchmarks, ChronoFlow achieves strong improvements in generation fidelity over existing baselines, while factorization studies support the proposed hierarchy. Our code is available at https://anonymous.4open.science/r/ChronoFlow.

Changhun Kim, Sunguk Jang, Jeongjun Lee +5

University of Wisconsin–Madison · AITRICS · KAIST

Irregular Time-Series ModelingFlow MatchingTime Series GenerationFlow-Based Generative Modeling
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Sep 21, 2026

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

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.

Subo Wei, Jianqi Gao, Mingyan Fan +3

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

Time Series GenerationAutoregressive GenerationTime Series TokenizationConditional Generative Modeling
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Sep 18, 2026

Time series generation with spectrally aligned latent flow matching

Latent flow models have proven to be a reliable and cost-effective method for time series generation. However, the latent compression induces unwanted artefacts, such as a spectral mismatch with respect to the underlying dataset, thus hindering their use as training surrogates. In this article, we propose a spectrally-aligned latent-flow time series generator, where the latent space for flow matching is trained to preserve dynamical properties that are relevant for the suitability of synthetic samples. We find that incorporating fine-tuning losses based on canonical signal representations such as the Fourier, wavelet and signature transforms helps overcome these issues. The interpretability of these transformations allows us to ensure that the synthetic signals are aligned with the true ones in terms of relevant features, such as smoothness or targeted spectral content, as opposed to relying on pointwise reconstruction losses only. We compare the proposed aligned models against a base latent-flow model and the state of the art over real-world long-range univariate and multivariate benchmark datasets. Our quantitative results validate the superiority of the proposed method in terms of its performance on metrics reflecting signal realness and computational efficiency, while being aligned to the training set with respect to its local structure.

Camilo Carvajal Reyes, Felipe Tobar

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

Time Series GenerationFlow-Based Generative ModelingLatent Space Alignment
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Sep 14, 2026

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

This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of labeled time-series data in real-world manufacturing settings, which hinders the development of robust machine learning models, we explore the potential of LLMs to learn complex temporal dependencies and generate realistic synthetic data. Our approach involves fine-tuning pre-trained LLMs on manufacturing process instructions and employing a Retrieval Augmented Generation (RAG) technique to enhance data diversity and realism. We evaluate our method against traditional time series modeling techniques like ARIMA and LSTMs, using quantitative metrics, PCA analysis, and downstream task performance (anomaly detection). Results demonstrate that our LLM-driven framework outperforms these baselines, generating high-quality synthetic time series data that effectively captures temporal dependencies and statistical properties of real manufacturing data, leading to improvements in downstream task performance.

Mantek Singh, Jeshwanth Challagundla, Prateek Karnal +3

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

ManufacturingSynthetic Data GenerationTime Series Generation
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Sep 14, 2026

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

Text-to-Time Series Generation (Text-to-TS) provides a promising paradigm for synthesizing time series from natural language, enabling scenario-specific generation when real observations are scarce or costly to acquire. However, existing methods typically lack an explicit mechanism for deriving generation logic from textual descriptions to guide time series synthesis. In this paper, we propose CodeTS, a verifiable framework that uses code as an intermediate generation interface, reformulating Text-to-TS generation as a Text-to-Code-to-TS process. CodeTS first maps textual temporal descriptions into an explicit code space, where executable code specifies how textual requirements shape target temporal patterns, and then obtains the time series through code execution. To learn this code generation process reliably without real code annotations, CodeTS constructs aligned Text-Code-TS triplets from structured temporal attributes for supervised initialization. More importantly, we further design multi-stage execution-based rewards that verify format validity, code executability, and time series quality, enabling real Text-TS pairs to provide training signals for Reinforcement Learning with Verifiable Rewards (RLVR). Extensive experiments on eight benchmarks across short, medium, and long generation lengths demonstrate that CodeTS provides a strong zero-shot solution for Text-to-TS generation, outperforming LLM-based baselines and achieving better averaged results than supervised generative baselines trained on the target datasets.

Xudong Yuan, Shunyu Liu, Tongya Zheng +3

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

Time Series Generation
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Sep 14, 2026

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

Machine learning can be crucial to help scale complex signal processing applications in scenarios such as healthcare. However, these machine learning models need rich datasets to be trained and there are often cases where it is not possible to access representative datasets. In this paper, we propose a deep generative model based on conditional variational autoencoders with the objective of augmenting the vital signs of healthy individuals in a way that mimics the patterns of a certain clinical condition. More specifically, we use a publicly available ICU (Intensive Care Unit) dataset to train our model and then evaluate it using the vital data that we have collected from healthy individuals. Our results demonstrate that the proposed model can not only learn the underlying dynamics of the ICU data but, more importantly, can reshape our collected data from healthy individuals in a way that is aligned with the vital signs of a certain clinical condition. We propose a distance metric that shows how our model can generate samples that are more aligned with the intended clinical labels when compared to the tested baselines.

Rafael Pina, Varuna De Silva, Mindula Illeperuma

Institute for Digital Technologies Loughborough University London London, United Kingdom

Synthetic Data AugmentationTime Series GenerationTime Series Data AugmentationConditional Generative Modeling
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Sep 4, 2026

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

Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable clinical prediction models. However, existing methods mainly focus on matching the overall distribution and temporal dynamics of real data, which does not necessarily ensure strong downstream utility on imbalanced medical datasets. Clinically informative patterns often occur at heterogeneous temporal scales, while rare minority-class characteristics can be obscured by dominant population patterns. To address these challenges, we propose MedFlow, a class-aware multi-scale flow matching framework for medical time-series synthesis. MedFlow employs a vector-quantized multi-scale tokenizer to represent medical sequences at complementary temporal resolutions, capturing both coarse clinical trends and fine-grained dynamics. We further introduce Token Marginal Guidance, which incorporates class-conditional token statistics directly into the flow matching process to steer generation toward class-specific regions of the learned tokens. This mechanism strengthens minority-class patterns, while preserving the global and tail distributions of real data. Experiments on four public datasets covering electronic health records, EEG, and ECG signals demonstrate that MedFlow consistently outperforms recent state-of-the-art diffusion-based baselines across downstream prediction tasks. On average, it improves AUPRC by 5.8%, reduces Context-FID by 88.6%, and achieves 3.8×\times higher sampling throughput.

Yanhao Huang, Shibo Feng, Wanjin Feng +2

Shanghai Jiao Tong University · Nanyang Technological University · Tsinghua University

Flow MatchingSynthetic Data GenerationTime Series GenerationConditional Generative Modeling
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