Abstract
ABSTRACT: Forecast the price of agricultural goods is a beneficial action for farmers, marketing agents, consumers, and policymakers. Today, managing this product security requires price forecasting models that are both efficient and reliable for a country’s import and export. In the last few decades, the Autoregressive Integrated Moving Average (ARIMA) model has been widely used in economics time series forecasting. Recently, many of the time series observations presented in economics have been clearly shown to be nonlinear, Machine learning (ML) modelling, conversely, offers a potential price forecasting technique that is more flexible given the limited data available in most countries’ economies. In this research, a hybrid price forecasting model has been used, through a novel clustering technique, a new cluster selection algorithm and a multilayer perceptron neural network (MLPNN), which had many advantages and using monthly time series of Thai rice FOB price form November 1987 to October 2017. The empirical results of this study showed that the value of root mean square error (RMSE) equals 14.37 and the Mean absolute percentage error (MAPE) equals 4.09% for the hybrid model. The evaluation results of proposed method and comparison its performance with four benchmark models, by monthly time series of Thailand rice FOB price from November 1987 to October 2017 showed the outperform of proposed method.
Highlights
Price variation through time usually caused by seasonal variation in prices, annual price behavior, long run trends in price, cyclical price behavior and government‘s intervention
The length of the lagging window is equal to the number of inputs for each multilayer perceptron (MLP) so that M = 6 is the number of neurons in the hidden layer of multilayer perceptron neural network (MLPNN) and outputs occupy the network structure
Performance indicators used in this study are: Mean absolute percentage error (MAPE), root mean square error (RMSE)
Summary
The history of studying the forecast of prices and production of agricultural products in the market is almost a century (JAYARAMU, 2015). Economic forecasting in the agricultural sector has had similarities with business and macroeconomic forecasting, but over time, the focus on itself has increased and expanded (ALLEN, 1994). Agricultural commodity prices, as well as the price of other products, were on an increasing trend since 2002 (GULERCE & UNAL, 2017). In recent years we have seen an increase in price fluctuations in most agricultural commodities, especially strategic commodities such as rice. This issue can have a direct and major impact on food security, both at the micro and macro levels of society. Price variation through time usually caused by seasonal variation in prices, annual price behavior, long run trends in price, cyclical price behavior and government‘s intervention
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