Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

Authors

  • Suhartono Suhartono Department of Statistics Institut Teknologi Sepuluh Nopember
  • Dedy Dwi Prastyo Department of Statistics Institut Teknologi Sepuluh Nopember
  • Heri Kuswanto Department of Statistics Institut Teknologi Sepuluh Nopember
  • Muhammad Hisyam Lee Department of Mathematical Science Universiti Teknologi Malaysia

DOI:

https://doi.org/10.11113/matematika.v34.n1.1040

Abstract

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

Author Biographies

Suhartono Suhartono, Department of Statistics Institut Teknologi Sepuluh Nopember

Statistics

Dedy Dwi Prastyo, Department of Statistics Institut Teknologi Sepuluh Nopember

Statistics

Heri Kuswanto, Department of Statistics Institut Teknologi Sepuluh Nopember

Statistics

Muhammad Hisyam Lee, Department of Mathematical Science Universiti Teknologi Malaysia

Mathematical Science

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Published

2018-05-28

How to Cite

Suhartono, S., Prastyo, D. D., Kuswanto, H., & Lee, M. H. (2018). Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 34(1), 103–111. https://doi.org/10.11113/matematika.v34.n1.1040

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Articles