Time series regression and artificial neural network approaches for forecasting unit price of tea at Colombo auction

Abstract

Tea export plays a vital role in the Sri Lankan economy. It is of immense importance to forecast the prices in the Colombo Tea Auction Center (CTAC) at which a majority of the Sri Lankan tea is marketed. There was no evidence of former studies on forecasting prices of tea at CTAC. The most familiar and the standard practice in the conventional context for forecasting a series varying with time is the building of time series models based on the stationarity and the characteristics of the relevant series, which are autoregressive (AR) terms and moving average (MA) terms. But the auction prices of tea are inherently noisy, non-stationary and chaotic in nature and therefore, the conventional methods cannot be applied. Alternatively, time series regression with generalized least squares and artificial neural network (ANN) were identified as two suitable methods for forecasting the price for a unit of Sri Lankan tea at the CTAC one month ahead. Models were fitted using the prices in 160 months at seven tea auction centers worldwide and assessed and compared using the mean absolute percentage error (MAPE), mean squared error (MSE), coefficient of determination and correlation coefficient between observed and fitted values. Both methods were found to perform well, ANN performing slightly better.