What is it about?
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.
Featured Image
Photo by Megan Thomas on Unsplash
Why is it important?
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.
Perspectives
In this research, the main goal is to investigate price determinants in agricultural markets and design machine learning strategies to forecast commodity prices in Malaysia. To achieve higher forecast accuracy, differential evolution could be employed for optimal lag selection by discarding irrelevant or redundant time lags. Being able to accurately forecast the agricultural prices, government can formulate new policies to intervene the potential situations occurs in the agricultural market. Meanwhile, farmers can make better decision on the plantation plan to improve the crop productivity.
Dr zhiyuan chen
The University of Nottingham Malaysia
Read the Original
This page is a summary of: Determining Optimal Lag Time Selection Function with Novel Machine Learning Strategies for Better Agricultural Commodity Prices Forecasting in Malaysia, August 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3417473.3417480.
You can read the full text:
Resources
Contributors
The following have contributed to this page
