AFSTGCN: Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

Xiao, Yuteng; Xia, Kaijian; Yin, H.; Zhang, Yudong; Qian, Zhenjiang; Liang, Yuehan; Li, Xiaodan

doi:10.1016/j.dcan.2022.06.019

Cited by 19 publications

(3 citation statements)

References 18 publications

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“…These models can better describe the spatiotemporal characteristics of urban traffic, but they do not address financial issues and are therefore not feasible for finance. As mentioned, Xiao et al (2021) and Xiao et al (2022) proposed two models to predict multivariate time series with spatiotemporal correlation, but the processes are fairly complex and model results do not reflect the spatiotemporal correlation coefficient. Accordingly, these models may encounter some difficulties in practical application, especially for people lacking a machine learning theoretical background.…”

Section: Literature Review and Model Designmentioning

confidence: 99%

“…The models characterizing spatiotemporal correlation are rarely applied in finance. The reason may be the temporal, spatial, and fused spatiotemporal relationships among the variables are not available from the ground truth (Xiao et al 2022). To date, studies on spatiotemporal correlations in the financial arena are limited.…”

Section: Introductionmentioning

confidence: 99%

“…To date, studies on spatiotemporal correlations in the financial arena are limited. Xiao et al (2021) proposed a convolutional LSTM network model; Xiao et al (2022) constructed an adaptive fused spatial-temporal graph convolutional network to predict a multivariate time series. However, both models are based on a deep-learning algorithm that results in a relatively complex process of predicting multivariate time series with spatiotemporal correlation characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic spatiotemporal correlation coefficient based on adaptive weight

Tan

Zhang

et al. 2023

Financ Innov

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Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk. Among various correlations, spatiotemporal correlations involve economic entity attributes and are interrelated in space and time. Such correlations have therefore drawn increasing attention in financial risk management. However, classical correlation measurements are typically based on either time series correlations or spatial dependence; they cannot be directly applied to financial data with spatiotemporal correlations. The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can (1) address the absolute quantity, dynamic quantity, and dynamic development of financial data and (2) be used for risk grading, financial risk evaluation, and portfolio management. To verify the validity and superiority of this model, cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation, respectively. Empirical findings show that the proposed coefficient is highly effective and convenient compared to others. Overall, our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.

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Section: Literature Review and Model Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%