A Comparison of Various Forecasting Methods for Autocorrelated Time Series

Abstract

The accuracy of forecasts significantly affects the overall performance of a whole supply chain system. Sometimes, the nature of consumer products might cause difficulties in forecasting for the future demands because of its complicated structure. In this study, two machine learning methods, artificial neural network (ANN) and support vector machine (SVM), and a traditional approach, the autoregressive integrated moving average (ARIMA) model, were utilized to predict the demand for consumer products. The training data used were the actual demand of six different products from a consumer product company in Thailand. Initially, each set of data was analysed using Ljung-Box-Q statistics to test for autocorrelation. Afterwards, each method was applied to different sets of data. The results indicated that the SVM method had a better forecast quality (in terms of MAPE) than ANN and ARIMA in every category of products.