Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
On the constrained time-series generation problem
A Coletta, S Gopalakrishnan… - Advances in Neural …, 2024 - proceedings.neurips.cc
Synthetic time series are often used in practical applications to augment the historical time
series dataset, amplify the occurrence of rare events and also create counterfactual …
series dataset, amplify the occurrence of rare events and also create counterfactual …
Tsgbench: Time series generation benchmark
Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data
augmentation, anomaly detection, and privacy preservation. Although significant strides …
augmentation, anomaly detection, and privacy preservation. Although significant strides …
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
Generative modeling of regular and irregular time series data via koopman VAEs
Generating realistic time series data is important for many engineering and scientific
applications. Existing work tackles this problem using generative adversarial networks …
applications. Existing work tackles this problem using generative adversarial networks …
Transfusion: generating long, high fidelity time series using diffusion models with transformers
The generation of high-quality, long-sequenced time-series data is essential due to its wide
range of applications. In the past, standalone Recurrent and Convolutional Neural Network …
range of applications. In the past, standalone Recurrent and Convolutional Neural Network …
Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models
Abstract Objective Electronic health records (EHRs) are rich sources of patient-level data,
offering valuable resources for medical data analysis. However, privacy concerns often …
offering valuable resources for medical data analysis. However, privacy concerns often …
Diffusion-ts: Interpretable diffusion for general time series generation
X Yuan, Y Qiao - arXiv preprint arXiv:2403.01742, 2024 - arxiv.org
Denoising diffusion probabilistic models (DDPMs) are becoming the leading paradigm for
generative models. It has recently shown breakthroughs in audio synthesis, time series …
generative models. It has recently shown breakthroughs in audio synthesis, time series …
Fast and reliable generation of ehr time series via diffusion models
Electronic Health Records (EHRs) are rich sources of patient-level data, including laboratory
tests, medications, and diagnoses, offering valuable resources for medical data analysis …
tests, medications, and diagnoses, offering valuable resources for medical data analysis …
PCF-GAN: generating sequential data via the characteristic function of measures on the path space
Generating high-fidelity time series data using generative adversarial networks (GANs)
remains a challenging task, as it is difficult to capture the temporal dependence of joint …
remains a challenging task, as it is difficult to capture the temporal dependence of joint …