Time Series and Neural Network Approaches for Accurate Forecasting of Monthly Natural Rubber Prices in Sri Lanka

Abstract

Rubber is the most significant and widely used material in the industry. Sri Lanka is well renowned for production of quality rubber. This study is undertaken to forecast natural rubber (NR) prices for upcoming years in Sri Lanka. Since rubber is a storable intermediate good, current population heavily depends on future prices. Before the year 2011, rubber has good price scale, but due to the lack of government intervention the rubber price has been decreased after 2011. With change of rubber price, the accurate forecast is extremely important in executing policies and making decisions for the future of rubber industry. Moreover, there is no research studies can be found which attempts to forecast monthly NR prices in Sri Lanka by using machine learning techniques. Monthly rubber prices from June 2005 to January 2019 was considered for this study. 80% of the data was used to fit the model and the rest was used for model performance evaluation. All the unit root tests confirmed that the first difference of log series was stationary. ARIMA (1,1,1) model was selected as the best model with lower Akaike Information Criterion (AIC) among the other candidate models which exhibits RMSE of 22.5039 and MAE of 16.6923. To find a better model which cater the instability of the NR prices properly, a dynamic machine learning technique, Time Delay Neural Network (TDNN) was employed. The architecture of the identified TDNN model with the hyper-parameter tunning consists of one hidden layer with sixteen neurons, and 1:16 time delays and exhibits lower errors: RMSE of 0.01347 and MAE of 0.0074. It can be concluded that TDNN perform better than the ARIMA model in forecasting NR prices Sri Lanka.