GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network

• URL: http://arxiv.org/abs/2306.01999v1
• Date: Sat, 3 Jun 2023 04:23:49 GMT
• Title: GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network
• Authors: Srikrishna Iyer and Teng Teck Hou
• Abstract summary: We propose a Graph-Attention-based Generative Adversarial Network (GAT-GAN) GAT-GAN generates long time-series data of high fidelity using an adversarially trained autoencoder architecture. We introduce a Frechet Inception distance-like (FID) metric for time-series data called Frechet Transformer distance (FTD) score (lower is better) to evaluate the quality and variety of generated data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GANs) have proven to be a powerful tool for generating realistic synthetic data. However, traditional GANs often struggle to capture complex relationships between features which results in generation of unrealistic multivariate time-series data. In this paper, we propose a Graph-Attention-based Generative Adversarial Network (GAT-GAN) that explicitly includes two graph-attention layers, one that learns temporal dependencies while the other captures spatial relationships. Unlike RNN-based GANs that struggle with modeling long sequences of data points, GAT-GAN generates long time-series data of high fidelity using an adversarially trained autoencoder architecture. Our empirical evaluations, using a variety of real-time-series datasets, show that our framework consistently outperforms state-of-the-art benchmarks based on \emph{Frechet Transformer distance} and \emph{Predictive score}, that characterizes (\emph{Fidelity, Diversity}) and \emph{predictive performance} respectively. Moreover, we introduce a Frechet Inception distance-like (FID) metric for time-series data called Frechet Transformer distance (FTD) score (lower is better), to evaluate the quality and variety of generated data. We also found that low FTD scores correspond to the best-performing downstream predictive experiments. Hence, FTD scores can be used as a standardized metric to evaluate synthetic time-series data.

Related papers

• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio [17.811771707446926]
  We show that learning based methods can, even based on synthetic data, significantly outperform GCC-PHAT on novel real world data. We provide our trained model, SONNET, which is runnable in real-time and works on novel data out of the box for many real data applications.
  arXiv  Detail & Related papers  (2024-11-20T10:23:21Z)
• FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective [48.00240550685946]
  Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies, respectively. We propose a novel Fourier Graph Neural Network (FourierGNN) by stacking our proposed Fourier Graph Operator (FGO) to perform matrix multiplications in Fourier space. Our experiments on seven datasets have demonstrated superior performance with higher efficiency and fewer parameters compared with state-of-the-
  arXiv  Detail & Related papers  (2023-11-10T17:13:26Z)
• Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data [50.84488941336865]
  We propose a novel method called Fully- Spatial-Temporal Graph Neural Network (FC-STGNN) For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances. For graph convolution, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations.
  arXiv  Detail & Related papers  (2023-09-11T08:44:07Z)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network [4.989480853499916]
  Time-series data is one of the most common types of data used in medical machine learning applications. We introduce TTS-GAN, a transformer-based GAN which can successfully generate realistic synthetic time-series data sequences. We use visualizations and dimensionality reduction techniques to demonstrate the similarity of real and generated time-series data.
  arXiv  Detail & Related papers  (2022-02-06T03:05:47Z)
• Towards Generating Real-World Time Series Data [52.51620668470388]
  We propose a novel generative framework for time series data generation - RTSGAN. RTSGAN learns an encoder-decoder module which provides a mapping between a time series instance and a fixed-dimension latent vector. To generate time series with missing values, we further equip RTSGAN with an observation embedding layer and a decide-and-generate decoder.
  arXiv  Detail & Related papers  (2021-11-16T11:31:37Z)
• Networked Time Series Prediction with Incomplete Data [59.45358694862176]
  We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. We conduct extensive experiments on three real-world datasets under different missing patterns and missing rates.
  arXiv  Detail & Related papers  (2021-10-05T18:20:42Z)
• PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series [0.0]
  We present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality. We show that PSA-GAN can be used to reduce the error in two downstream forecasting tasks over baselines that only use real data.
  arXiv  Detail & Related papers  (2021-08-02T15:30:15Z)
• Conditional GAN for timeseries generation [0.0]
  Time Series GAN (TSGAN) is proposed to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database.
  arXiv  Detail & Related papers  (2020-06-30T02:19:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.