Unobserved Components Model for Forecasting Sugarcane Production in Sri Lanka

Unobserved Components Model (UCM) is a structural time series model and it can decompose the response series into latent components, such as trend, cycle and seasonal effects and linear and nonlinear regression effects. The UCM combines the capabilities of Autoregressive Integrated Moving Average (ARIMA) model with interpretability of smoothing models. This study was carried out to forecast sugarcane production in Sri Lanka using UCM model. The best fitting model was selected based on Akaike information criterion (AIC) and Bayesian Information Criterion (BIC), followed by residual analysis. The selected model was used to make sample period forecasts (From 1979 - 2013) and post sample period forecasts (From 2014 to 2018). Forecasting accuracy of model was evaluated using the Mean Absolute Percentage Error (MAPE). Linear trend model (adj. R²=77 %) with zero variance slope and two cycles was selected as best among the tested UCM models for cane production data. MAPE was 10.56 % for sample period forecasts and 4.01 % for post-sample period forecasts. Predicted cane production for year 2019 was 813,888 ± 293,891 tons.