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A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions

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Abstract

With the rapid development of data acquisition and storage technology, spatio-temporal (ST) data in various fields are growing explosively, so many ST prediction methods have emerged. The review presented in this paper mainly studies the prediction of ST series. We propose a new taxonomy organized along three dimensions: ST series prediction methods (focusing on time feature learning, focusing on spatial feature learning, and focusing on spatial–temporal feature learning), techniques of ST series prediction (the RNN-, CNN-, and transformer-based models, as well as the CNN-based-composite model and GNN-based-composite models, and the miscellaneous model) and ST series prediction results (single target and multi-target). We first introduce and explain each dimension of the taxonomy in detail. After providing this three-dimensional view, we comprehensively review and compare the recent related ideas in the literature and analyze their advantages and limitations. Moreover, we summarize the key information of the existing literature and provide guidance for researchers to select suitable models. Second, we summarize the different applications of deep learning models in ST series prediction based on current literature and list relevant datasets and download links per application classifications. Lastly, we comprehensively analyze the current innovation and challenges and suggest future directions for researching ST series prediction after comparing and analyzing the computing performance of these forecasting models. In addition, each method or model solves one aspect of the challenge, which means that two or more methods should be combined to solve more challenges at the same time. We hope this article provides readers a broader and deeper understanding of the field of ST series research.

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Acknowledgements

This research is supported by Defense Industrial Technology Development Program, Grant/Award Number: JCKY2020601B018; Research Fund of Jinling Institute of Technology for Advanced Talents, Grant/Award Number: jit-b-201805. The authors would like to thank Haijun Zhang, the associate editor of Neural Computing and Applications, and anonymous reviewers for their insightful comments and suggestions. As a result, this paper has been improved substantially.

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  1. Army Engineering University of PLA, Nanjing, China

    Feiyan Sun, Wenning Hao & Ao Zou

  2. Jinling Institute of Technology, Nanjing, China

    Feiyan Sun & Qianyan Shen

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Appendices

Appendix 1. Methods

See Table 12.

Table 12 Keywords for selection and exclusion
Full size table

Appendix 2. Table Note

Here, we provide keys to help read the tables in the paper,

Results means whether the method does single-target predictions and predicts multi-target simultaneously.

Loss and metrics indicates the loss of training and metrics of evaluation. Because the definitions can be found in relevant papers, we provide only explanations of abbreviations here: mean absolute error (MAE), mean relative error (MRE), mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE, RMSE, MSE), L1 loss (MAE), L2 loss (MSE), quantile loss (QL), empirical correlation coefficient (CORR), root relative squared error (RRSE), negative log-likelihood (NLL), ρ-quantile loss R_ρ with ρϵ(0,1), and symmetric mean absolute percentage error (sMAPE).

Structure refers to the different combinations of time and spatial modeling, including series, parallel, and fusion structures. Series structure models one dimension first, using the output obtained as input for modeling another dimension, and then models the other dimension. One example is modeling the temporal dependency relationship of input features first, using the resulting output as input to the spatial relationship extraction module, conducting spatial modeling, and finally obtaining the final predicted value. Parallel structure means the input sequence is simultaneously input into both the time and spatial networks for learning temporal and spatial dependencies. The obtained time and spatial network information are fused before being applied as the input sequence to the next layer. After an intervention round, further learning is done to obtain the final prediction result. Fusion structure refers to time modeling and spatial modeling that are not independent but cross-integrated, for example, when time modeling is conducted, each time step incorporates the spatial information of the nodes rather than simply their own time series.

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Sun, F., Hao, W., Zou, A. et al. A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions. Neural Comput & Applic 36, 9919–9943 (2024). https://doi.org/10.1007/s00521-024-09659-1

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