Hybrid Fourier Regression – Multilayer Perceptrons Neural Network for forecasting

Article type: Research Article

Authors: Fajar, Muhammad^* | Nurfalah, Zelani

Affiliations: BPS-Statistics Indonesia, Jakarta, Indonesia

Correspondence: [*] Corresponding author: Muhammad Fajar, BPS-Statistics Indonesia, Jakarta, Indonesia. E-mail: [email protected].

Abstract: Forecasting methods are advantageous tools to predict the future, especially for agricultural commodities production. This study aims to compare the forecasting method between Fourier Regression, Multilayer Perceptrons Neural Networks (MPNN), and introducing a new forecasting method hybrid Fourier Regression – Multilayer Perceptrons Neural Networks Model proposed by the author. These methods are applied to forecast the production of big chili commodities since it is one of the essential vegetable commodities with a high household and industrial consumption in Indonesia. The big chili production data used is monthly from January 2010 to June 2017 (in quintal units) sourced from Statistics Indonesia. The results show hybrid Fourier Regression – Multilayer Perceptrons Neural Networks Model is more accurate to forecast big chili production than Fourier Regression and Multilayer Perceptrons (MPNN). The MAPE produced by Fourier Regression-MPNN is the lowest compared to the other methods, which is 4.45. In summary, the use of the hybrid Fourier Regression-MPNN method in forecasting big chili production can help the government to find out the potential production of big chili in the next few quarters. Furthermore, the results are useful for considering some government policies about big chili needs such as making a decision to export or import big chili commodities.

Keywords: Hybrid, forecasting, fourier, neural network, perceptron

DOI: 10.3233/SJI-210876

Journal: Statistical Journal of the IAOS, vol. 37, no. 4, pp. 1199-1204, 2021