Time Series Generation
22 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Time Series Generation
Most implemented papers
Time Series Generation with Masked Autoencoder
This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a scalable self-supervised model for time series generation.
Multi-Label Clinical Time-Series Generation via Conditional GAN
luchang-cs/mtgan
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In this work, we propose a Multi-label Time-series GAN (MTGAN) to generate EHR and simultaneously improve the quality of uncommon disease generation.
Deep Latent State Space Models for Time-Series Generation
alexzhou907/ls4
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Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series.
Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric
pere98diaz/neural-sdes-for-conditional-time-series-generation-and-the-signature-wasserstein-1-metric
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To overcome this, we propose the use of \textit{Conditional Neural Stochastic Differential Equations}, which have a constant memory cost as a function of depth, being more memory efficient than traditional deep learning architectures.
Causal Recurrent Variational Autoencoder for Medical Time Series Generation
We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process.
PCF-GAN: generating sequential data via the characteristic function of measures on the path space
deepintostreams/pcf-gan
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NeurIPS 2023
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 probability distributions induced by time-series data.
Non-adversarial training of Neural SDEs with signature kernel scores
issaz/sigker-nsdes
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NeurIPS 2023
Neural SDEs are continuous-time generative models for sequential data.
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.
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
ml4its/timevqvae-anomalydetection
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We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability.
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation
Lysarthas/Time-Transformer
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Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties.
