Papers Time Series Generation
“Time Series Generation” 태그가 달린 논문 87편 · 필터 해제
How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control
Recent advances in time series generation have shown promise, yet controlling properties in generated sequences remains challenging. Time Series Editing (TSE) - making precise modifications while preserving temporal cohe…
DenoisingTime SeriesTime Series GenerationTrajectory Generator Matching for Time Series
Accurately modeling time-continuous stochastic processes from irregular observations remains a significant challenge. In this paper, we leverage ideas from generative modeling of image data to push the boundary of time s…
Time SeriesTime Series GenerationForging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides synthetic time series generation from a few samp…
Language ModelingLanguage ModellingLarge Language ModelSynthetic Data Generation+2Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data
The widespread adoption of mobile sensors has the potential to provide massive and heterogeneous time series data, driving Artificial Intelligence applications in mHealth. However, data collection remains limited due to …
Data AugmentationSynthetic Data GenerationTime SeriesTime Series GenerationMSDformer: Multi-scale Discrete Transformer For Time Series Generation
Discrete Token Modeling (DTM), which employs vector quantization techniques, has demonstrated remarkable success in modeling non-natural language modalities, particularly in time series generation. While our prior work S…
Model OptimizationTime SeriesTime Series GenerationDiffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
This paper presents a modified model predictive control (MPC) framework for real-time power system operation. The framework incorporates a diffusion model tailored for time series generation to enhance the accuracy of th…
Load ForecastingModel Predictive ControlTime SeriesTime Series GenerationT2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models
Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series datasets across domains. While diffusion models have a…
Time SeriesTime Series GenerationTarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation
Synthetic Electronic Health Record (EHR) time-series generation is crucial for advancing clinical machine learning models, as it helps address data scarcity by providing more training data. However, most existing approac…
Synthetic Data GenerationTime SeriesTime Series GenerationDiffusion Transformers for Tabular Data Time Series Generation
Tabular data generation has recently attracted a growing interest due to its different application scenarios. However, generating time series of tabular data, where each element of the series depends on the others, remai…
Tabular Data GenerationTime SeriesTime Series GenerationVideo GenerationTheoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency
Recent studies suggest utilizing generative models instead of traditional auto-regressive algorithms for time series forecasting (TSF) tasks. These non-auto-regressive approaches involving different generative methods, i…
Time SeriesTime Series ForecastingTime Series GenerationWaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion Models
Generating temporal data under constraints is critical for forecasting, imputation, and synthesis. These datasets often include auxiliary conditions that influence the values within the time series signal. Existing metho…
DenoisingImputationTime SeriesTime Series GenerationCoFinDiff: Controllable Financial Diffusion Model for Time Series Generation
The generation of synthetic financial data is a critical technology in the financial domain, addressing challenges posed by limited data availability. Traditionally, statistical models have been employed to generate synt…
DiversitySynthetic Data GenerationTime SeriesTime Series GenerationBRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling
Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown promise in unconditional single-domain T…
counterfactualData AugmentationTime SeriesTime Series GenerationRobust time series generation via Schrödinger Bridge: a comprehensive evaluation
We investigate the generative capabilities of the Schr\"odinger Bridge (SB) approach for time series. The SB framework formulates time series synthesis as an entropic optimal interpolation transport problem between a ref…
Image GenerationTime SeriesTime Series GenerationClosing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping
In this paper, we introduce Neural Mapper for Vector Quantized Time Series Generator (NM-VQTSG), a novel method aimed at addressing fidelity challenges in vector quantized (VQ) time series generation. VQ-based methods, s…
Time SeriesTime Series ClassificationTime Series GenerationCENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios
Recent breakthroughs in large-scale generative modeling have demonstrated the potential of foundation models in domains such as natural language, computer vision, and protein structure prediction. However, their applicat…
Protein Structure PredictionTime SeriesTime Series GenerationTime series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin
The research related to digital twins has been increasing in recent years. Besides the mirroring of the physical word into the digital, there is the need of providing services related to the data collected and transferre…
Time SeriesTime Series ForecastingTime Series GenerationTimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. W…
Data AugmentationTime SeriesTime Series GenerationAVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series Generation
Data augmentation can significantly enhance the performance of machine learning tasks by addressing data scarcity and improving generalization. However, generating time series data presents unique challenges. A model mus…
Data AugmentationTime SeriesTime Series GenerationPopulation Aware Diffusion for Time Series Generation
Diffusion models have shown promising ability in generating high-quality time series (TS) data. Despite the initial success, existing works mostly focus on the authenticity of data at the individual level, but pay less a…
Time SeriesTime Series Generation