VAR and GSTAR-Based Feature Selection in Support Vector Regression for Multivariate Spatio-Temporal Forecasting

  • Dedy Dwi PrastyoEmail author
  • Feby Sandi Nabila
  • Suhartono
  • Muhammad Hisyam Lee
  • Novri Suhermi
  • Soo-Fen Fam
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)


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.


SVR VAR GSTAR Feature selection Rainfall 



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.


  1. 1.
    Kuswanto, H., Salamah, M., Retnaningsih, S.M., Prastyo, D.D.: On the impact of climate change to agricultural productivity in East Java. J. Phys: Conf. Ser. 979(012092), 1–8 (2018)Google Scholar
  2. 2.
    Adams, R.M., Fleming, R.A., Chang, C.C., McCarl, B.A., Rosenzweig, C.: A reassessment of the economic effects of global climate change on U.S. agriculture. Clim. Change 30(2), 147–167 (1995)CrossRefGoogle Scholar
  3. 3.
    Schlenker, W., Lobell, D.B.: Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5(014010), 1–8 (2010)Google Scholar
  4. 4.
    Tsay, R.S.: Multivariate Time Series Analysis. Wiley, Chicago (2014)zbMATHGoogle Scholar
  5. 5.
    Suhartono, Prastyo, D.D., Kuswanto, H., Lee, M.H.: Comparison between VAR, GSTAR, FFNN-VAR, and FFNN-GSTAR models for forecasting oil production. Matematika 34(1), 103–111 (2018)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Haerdle, W.K., Prastyo, D.D., Hafner, C.M.: Support vector machines with evolutionary model selection for default prediction. In: Racine, J., Su, L., Ullah, A. (eds.) The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics, pp. 346–373. Oxford University Press, New York (2014)Google Scholar
  7. 7.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, Pittsburgh (1992)Google Scholar
  8. 8.
    Smola, A.J., Scholköpf, B.: A tutorial on support vector regression, statistics and computing. Stat. Comput. 14(3), 192–222 (2004)CrossRefGoogle Scholar
  9. 9.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machines classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefGoogle Scholar
  10. 10.
    Borovkova, S., Lopuhaä, H.P., Ruchjana, B.N.: Consistency and asymptotic normality of least squares estimators in Generalized STAR models. Stat. Neerl. 62(4), 482–508 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bonar, H., Ruchjana, B.N., Darmawan, G.: Development of generalized space time autoregressive integrated with ARCH error (GSTARI - ARCH) model based on consumer price index phenomenon at several cities in North Sumatra province. In: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II). AIP Conference Proceedings 1827 (020009), Bandung (2017)Google Scholar
  12. 12.
    Khotimah, C., Purnami, S.W., Prastyo, D.D., Chosuvivatwong, V., Spriplung, H.: Additive survival least square support vector machines: a simulation study and its application to cervical cancer prediction. In: Proceedings of the 13th IMT-GT International Conference on Mathematics, Statistics and their Applications (ICMSA). AIP Conference Proceedings 1905 (050024), Kedah (2017)Google Scholar
  13. 13.
    Khotimah, C., Purnami, S.W., Prastyo, D.D.: Additive survival least square support vector machines and feature selection on health data in Indonesia. In: Proceedings of the International Conference on Information and Communications Technology (ICOIACT). IEEE Xplore (2018)Google Scholar
  14. 14.
    Suhartono, Saputri, P.D., Amalia, F.F., Prastyo, D.D., Ulama, B.S.S.: Model selection in feedforward neural networks for forecasting inflow and outflow in Indonesia. In: Mohamed, A., Berry, M., Yap, B. (eds.) SCDS 2017. CCIS, vol. 788, pp. 95–105. Springer, Singapore (2017). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dedy Dwi Prastyo
    • 1
    Email author
  • Feby Sandi Nabila
    • 1
  • Suhartono
    • 1
  • Muhammad Hisyam Lee
    • 2
  • Novri Suhermi
    • 1
  • Soo-Fen Fam
    • 3
  1. 1.Department of StatisticsInstitut Teknologi Sepuluh NopemberSurabayaIndonesia
  2. 2.Department of Mathematical SciencesUniversiti Teknologi MalaysiaSkudaiMalaysia
  3. 3.Department of TechnopreneurshipUniversiti Teknikal Malaysia MelakaMelakaMalaysia

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