Abstract
Multivariate time series modeling is quite challenging particularly in term of diagnostic checking for assumptions required by the underlying model. For that reason, nonparametric approach is rapidly developed to overcome that problem. But, feature selection to choose relevant input becomes new issue in nonparametric approach. Moreover, if the multiple time series data are observed from different sites, then the location possibly play the role and make the modeling become more complicated. This work employs Support Vector Regression (SVR) to model the multivariate time series data observed from three different locations. The feature selection is done based on Vector Autoregressive (VAR) model that ignore the spatial dependencies as well as based on Generalized Spatio-Temporal Autoregressive (GSTAR) model that involves spatial information into the model. The proposed approach is applied for modeling and forecasting rainfall in three locations in Surabaya, Indonesia. The empirical results inform that the best method for forecasting rainfall in Surabaya is the VAR-based SVR approach.
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Acknowledgement
This research was supported by DRPM under the scheme of “Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT)” with contract number 930/PKS/ITS/2018. The authors thank to the General Director of DIKTI for funding and to the referees for the useful suggestions.
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Prastyo, D.D., Nabila, F.S., Suhartono, Lee, M.H., Suhermi, N., Fam, SF. (2019). VAR and GSTAR-Based Feature Selection in Support Vector Regression for Multivariate Spatio-Temporal Forecasting. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_4
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DOI: https://doi.org/10.1007/978-981-13-3441-2_4
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