Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 96-107
Abstract
This independent study attempts to find the most appropriate model for forecasting price of Ribbed Smoked Sheet No.3 in Thailand. Daily price from Rubber Authority of Thailand since 2011 to 2021 are used as raw data. A total of 2,618 values of daily rubber price are divided into training and test sets. The training set involves 2,376 values from 2011 to 2020. It is used for constructing four forecasting models i.e. moving average, Holt’s method, Box-Jenkins method and Neural network. The test set, including 242 values from 2021, is used for comparing accuracy of the forecast via cri-teria of the lowest. The finding indicates that the Neural network by non-linear autoregressive neural network (NNAR) is the most suitable for fore-casting price of Ribbed Smoked Sheet No.3. This method has the least Mean absolute error (MAE) of 4.5352, Root mean square error (RMSE) of 5.7807 and Mean absolute percentage error (MAPE) of 7.3309. Respectively, Box-Jenkins method, Moving average and Holt’s Method are found to provide less accurate result.