Forecasting techniques based on absolute difference for small dataset to predict the SET Index in Thailand

Abstract

This research aims to use a simple statistical method as a forecasting model with a small dataset. Absolute difference methods, average absolute difference and minimum absolute difference, were used to adjust the dataset, i.e., the SET Index, before fitting using the following two forecasting models, an autoregressive forecasting model and a simple moving average forecasting model. Then we compared the quality of predictions using the mean square error and the mean absolute difference. These showed that the mean square error of the average absolute difference filtering method were 15.13%, 15.17% and 7.31% less than the original dataset for a one-period autoregressive forecasting model, a two-period autoregressive forecasting model and a three-period simple moving average forecasting model, respectively. The mean absolute differences were 8.36% , 8.39% and 4.10% less than the original dataset for a one-period autoregressive forecasting model, a two-period autoregressive forecasting model and a three-period simple moving average forecasting model, respectively. The mean square error of the minimum absolute difference filtering method were 66.02%, 58.94% and 16.33% less than the original dataset for a one-period autoregressive forecasting model, a two-period autoregressive forecasting model and a three-period simple moving average forecasting model, respectively. The mean absolute differences were 39.60% , 33.81% and 9.37% less than the original dataset for a one-period autoregressive forecasting model, a two-period autoregressive forecasting model and a three-period simple moving average forecasting model, respectively.