Prediction of national strategic commodity prices based on multivariate nonparametric time series analysis

Abstract

Artificial Neural Network (ANN) or often referred to as artificial neural networks is a method inspired by the awareness of the complex learning system in the brain consisting of sets of neurons that are closely interconnected. While the Fourier series is a trigonometric polynomial function that has a very high degree of flexibility to overcome data that has a repeating pattern. In the time series data, both models can be used for nonparametric approaches that have many advantages, one of which is that they are more flexible and not tied to certain classical assumptions. In this study, a comparative study will be carried out between the ANN model and the Fourier series model to obtain forecasting results on national strategic food commodity prices simultaneously, with initial commodity prices referring to the website of Pusat Informasi Harga Pangan Strategis (PIHPS). The selection of the best model is selected based on the model that results in the smallest rate of prediction error in a commodity price prediction data by comparing the Mean Absolute Percentage Error (MAPE) values. The results of this test get the smallest MAPE value on the ANN model of 0.05974244, while the smallest MAPE value on the Fourier series model is 0.000325423. The results show that the Fourier series model is the best model for predicting the price of strategic commodities in Indonesia.