期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 13, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3474838
关键词
Generative adversarial nets; spatio-temporal data; time series; trajectory data
资金
- Australian Government through the Australian Research Council [LP150100246, DP190101485]
- RMIT Research Stipend Scholarship
- CSIRO Data61 Scholarship
- Australian Research Council [LP150100246] Funding Source: Australian Research Council
This article provides a comprehensive review of the recent developments of GANs for spatio-temporal data, including the application of popular GAN architectures and the common practices for evaluating the performance of spatio-temporal applications. Future research directions are also pointed out.
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 article, 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.
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