Generative Adversarial Networks for Spatio-temporal Data: A Survey

• URL: http://arxiv.org/abs/2008.08903v4
• Date: Fri, 30 Jul 2021 02:36:45 GMT
• Title: Generative Adversarial Networks for Spatio-temporal Data: A Survey
• Authors: Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim
• Abstract summary: GAN-based techniques are shown to be promising for point-based applications such as trajectory prediction, events generation and time-temporal data imputation. We have conducted a comprehensive review of recent developments of GANs fortemporal data.
• Score: 8.575750904153201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this paper, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

Related papers

• A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification [7.58512888676767]
  This review provides a comprehensive overview of approaches and application domains various GNNs for time series classification and forecasting. A database search was conducted, and over 150 journal papers were selected for a detailed examination of the current state-of-the-art in the field.
  arXiv  Detail & Related papers  (2024-10-29T08:05:10Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting [0.8215320771752098]
  This work aims at expanding upon a conventional-temporal graph neural network architecture. The end-product is a novel-temporal graph network neural architecture that is referred to as WEST (WEighted STacked) GCN-LSTM.
  arXiv  Detail & Related papers  (2024-05-01T15:19:19Z)
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision [0.0]
  Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, especially in the research field of computer vision. There have been many surveys on GANs, organizing the vast GAN literature from various focus and perspectives. This survey intends to bridge that gap and present some of the landmark research works on the theory and application of GANs, in chronological order.
  arXiv  Detail & Related papers  (2023-11-02T05:11:47Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection [98.41798478488101]
  Time series analytics is crucial to unlocking the wealth of information implicit in available data. Recent advancements in graph neural networks (GNNs) have led to a surge in GNN-based approaches for time series analysis. This survey brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.
  arXiv  Detail & Related papers  (2023-07-07T08:05:03Z)
• Graph Neural Network for spatiotemporal data: methods and applications [7.612070518526342]
  Graph neural networks (GNNs) have emerged as a powerful tool for understanding data with dependencies to each other. This article aims to provide an overview of the technologies and applications of GNNs in thetemporal domain.
  arXiv  Detail & Related papers  (2023-05-30T02:27:17Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• 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)
• Generative adversarial networks in time series: A survey and taxonomy [7.885673762715387]
  Generative adversarial networks (GANs) studies have grown exponentially in the past few years. GAN applications have diversified across disciplines such as time series and sequence generation. As a relatively new niche for GANs, fieldwork is ongoing to develop high quality, diverse and private time series data.
  arXiv  Detail & Related papers  (2021-07-23T09:38:51Z)

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.