Determining Optimal Lag Time Selection Function with Novel Machine Learning Strategies for Better Agricultural Commodity Prices Forecasting in Malaysia

Abstract

In 2018, agriculture remains an important sector of Malaysia's economy, contributing around 7.54% to national GDP and 11.09% to the total employment. However, in agriculture, price volatility is often unpredictable due to the reliability of agricultural production on natural phenomena. Instability of prices endanger the development of Malaysia's economy and the food accessibility by consumers, which may lead to food insecurity, hunger and malnutrition. Thus, the need of an accurate forecasting model for agricultural commodity price is acute, especially for government and farmers to propose new policies and better plantation plan to deal with potential risks in the markets. In the context of forecasting, optimal lag selection is important to improve the forecast performance in terms of time and accuracy. The main goal of this research is to study and design novel machine learning strategies with optimal lag time selection function to forecast agricultural commodity price more accurately in order to improve plantation plan in Malaysia.

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