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Time Series Generation

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Showing 1–50 of 87 papers

TitleStatusHype
How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control—0
Trajectory Generator Matching for Time Series—0
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation—0
Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data—0
MSDformer: Multi-scale Discrete Transformer For Time Series Generation—0
Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation—0
T2S: High-resolution Time Series Generation with Text-to-Series Diffusion ModelsCode2
TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation—0
Diffusion Transformers for Tabular Data Time Series GenerationCode1
Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency—0
WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion ModelsCode0
CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation—0
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling—0
Robust time series generation via Schrödinger Bridge: a comprehensive evaluation—0
Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural MappingCode2
CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios—0
Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin—0
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts—0
AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series GenerationCode0
Population Aware Diffusion for Time Series GenerationCode1
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting—0
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time SeriesCode0
FM-TS: Flow Matching for Time Series GenerationCode1
IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems—0
Federated Time Series Generation on Feature and Temporally Misaligned Data—0
SeriesGAN: Time Series Generation via Adversarial and Autoregressive LearningCode1
Constrained Posterior Sampling: Time Series Generation with Hard Constraints—0
ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series GenerationCode1
Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective—0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining—0
Leveraging Priors via Diffusion Bridge for Time Series Generation—0
Synthetic Time Series for Anomaly Detection in Cloud Microservices—0
AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models—0
TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation—0
SigDiffusions: Score-Based Diffusion Models for Long Time Series via Log-Signature EmbeddingsCode0
A Survey of Transformer Enabled Time Series Synthesis—0
High Rank Path Development: an approach of learning the filtration of stochastic processes—0
A Study of Posterior Stability for Time-Series Latent Diffusion—0
Application of time-series quantum generative model to financial data—0
Towards Controllable Time Series Generation—0
Time Weaver: A Conditional Time Series Generation Model—0
Diffusion-TS: Interpretable Diffusion for General Time Series GenerationCode3
Time series generation for option pricing on quantum computers using tensor network—0
TimEHR: Image-based Time Series Generation for Electronic Health RecordsCode0
Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review—0
Class Conditional Time Series Generation with Structured Noise Space GAN—0
Time-Transformer: Integrating Local and Global Features for Better Time Series GenerationCode1
Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation—0
Explainable Time Series Anomaly Detection using Masked Latent Generative ModelingCode1
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