Abstract
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.
Highlights
National level agricultural production forecasts are important for making policy decisions
No such attempts have been reported for forecasting national level sugarcane production in Sri Lanka
The main limitation of the Autoregressive Integrated Moving Average (ARIMA) approach is that it can be applied in the situations where either the series to be analysed is stationary, or it can be differenced into a stationary process (Koopman and Ooms, 2010)
Summary
National level agricultural production forecasts are important for making policy decisions. Yield-forecasting systems are available for major crops such as coconut, paddy, rubber, and tea in Sri Lanka. No such attempts have been reported for forecasting national level sugarcane production in Sri Lanka. Unobserved component models (UCM) can be used as an alternative approach to overcome these problems (Harvey, 1996; Koopman and Ooms, 2010). UCM model analyses and forecasts time series data by breaking down the response series into latent components that are useful in explaining and predicting its behavior such as trends, seasonal factors, cycles, and regression effects due to the predictor series. The UCM combines the skillfulness of the ARIMA model with interpretability of the smoothing model (Koopman and Harvey, 2003)
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