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dc.contributor.authorLkhagvadorj Munkhdalaien_US
dc.contributor.authorMeijing Lien_US
dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorKeun Ho Ryuen_US
dc.description.abstract© 2020, Springer Nature Switzerland AG. A determining the most relevant variables and proper lag length are the most challenging steps in multivariate time series analysis. In this paper, we propose a hybrid Vector Autoregressive and Gated Recurrent Unit (VAR-GRU) model to find the contextual variables and suitable lag length to improve the predictive performance for financial multivariate time series. VAR-GRU approach consists of two layers, the first layer is a VAR model-based variable and lag length selection and in the second layer, the GRU-based multivariate prediction model is trained. In the VAR layer, the Akaike Information Criterion (AIC) is used to select VAR order for finding the optimal lag length. Then, the Granger Causality test with the optimal lag length is utilized to define the causal variables to the second layer GRU model. The experimental results demonstrate that the ability of the proposed hybrid model to improve prediction performance against all base predictors in terms of three evaluation metrics. The model is validated over real-world financial multivariate time series dataset.en_US
dc.subjectComputer Scienceen_US
dc.titleVAR-GRU: A Hybrid Model for Multivariate Financial Time Series Predictionen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume12034 LNAIen_US Universityen_US Maritime Universityen_US National Universityen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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