FORECAST PERFORMANCE OF UNIVARIATE TIME SERIES AND ARTIFICIAL NEURAL NETWORK MODELS

  • A. I. Taiwo Olabisi Onabanjo University, Ago-Iwoye
  • S. O. Folorunso Olabisi Onabanjo University, Ago-Iwoye, Nigeria
  • Z. O. Ogunwobi Olabisi Onabanjo University, Ago-Iwoye, Nigeria
Keywords: Time series model, Artificial Neural Network, Periodic variation, Seasonal variation, Forecasting, Forecasting Evaluation

Abstract

In this paper, the better model for forecasting Nigeria monthly Precipitation time series data that exhibit seasonal, periodic variations and non-linearity is determined. The models considered are Seasonal Autoregressive Integrated Moving Average (SARIMA), Fourier Autoregressive (FAR) and Artificial Neural Networks (ANN) models. The accuracy of the out-sample forecast of the model considered was measured based on the following forecast evaluations sum of square error (SSE), Mean square error (MSE) and Root mean square error RMSE. From the results, the FAR model forecast was better than that of SARIMA model based on the values of the forecast evaluations when seasonal and period in the series is considered and ANN model forecast was better that both FAR and SARIMA when the non-linear nature of the precipitation is considered. In conclusion, the FAR model is the most appropriate model for forecasting seasonal and periodic variations while the ANN model is the most suitable model for forecasting non-linearity in Nigeria monthly precipitation time series data

Author Biographies

A. I. Taiwo, Olabisi Onabanjo University, Ago-Iwoye

Department of Mathematical Sciences

S. O. Folorunso, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

Department of Mathematical Sciences

Z. O. Ogunwobi, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

Department of Mathematical Sciences

Published
2019-03-19
How to Cite
Taiwo, A., Folorunso, S., & Ogunwobi, Z. (2019). FORECAST PERFORMANCE OF UNIVARIATE TIME SERIES AND ARTIFICIAL NEURAL NETWORK MODELS. LAUTECH Journal of Engineering and Technology, 12(2), 67-71. Retrieved from http://laujet.com/index.php/laujet/article/view/296
Section
Articles