Determination of Artificial Neural Network Structure with Autoregressive Form of ARIMA and Genetic Algorithm to Forecast Monthly Paddy Prices in Thailand

Abstract

This research aims to study a development of a forecasting model to predict a monthly paddy price in Thailand with 2 datasets. Each of datasets is the univariate time series that is a monthly data, since Jan 1997 to Dec 2017. To generate a forecasting model, we present a forecasting model by using the Artificial Neural Network technique and determine its structure with Autoregressive form of the ARIMA model and Genetic Algorithm, it's called AR-GA-ANN model. To generate the AR-GA-ANN model, we set 1 to 3 hidden layers for testing, determining the number of input nodes by an Autoregressive form of the ARIMA model and determine the number of neurons in hidden layer by Genetic Algorithm. Finally, we evaluate a performance of our AR-GA-ANN model by error measurement with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) and compare errors with the ARIMA model. The result found that all of AR-GA-ANN models have lower RMSE and MAPE than the ARIMA model and the AR-GA-ANN with 1 hidden layer has lowest RMSE and MAPE in both datasets.

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

“…Some other authors like, for instance, Alameer et al (2019b) and Weng et al (2018) used them to estimate the parameters of models such as the unmodified adaptive neuro-fuzzy inference system (ANFIS) or regularization of the extreme learning machine (ELM). They can also be used to optimize parameters in the case of such models as the LSSVR model from Yuan et al (2020), specifying the parameters of the ENN by Shahwan and Odening (2007), or determining the number of electrons in ANN hidden layers (Chuentawat and Loetyingyot 2019).…”

“…Meanwhile, Shih et al (2009) optimized the weights of the case-based reasoning model to forecast broiler prices. Chuentawat and Loetyingyot (2019) employed a genetic algorithm to determine the number of neurons in the hidden layer of an ANN. Their task was to construct a forecasting model of rice prices in Thailand.…”

“…In such hybrids, genetic algorithms are used to improve chosen model results and sometimes to overcome their flaws. For example, genetic algorithms can optimize their parameters, and in the case of neural networks, they can find the optimal number of neurons in each hidden layer, or they can help in finding the global optimum (Chiroma et al 2015;Shahwan and Odening 2007;Chuentawat and Loetyingyot 2019).…”