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Forecasting techniques based on absolute difference for small dataset to predict the SET Index in Thailand |
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| รหัสดีโอไอ | |
| Creator | 1. Watcharin Klongdee 2. Mathee Pongkitwitoon |
| Title | Forecasting techniques based on absolute difference for small dataset to predict the SET Index in Thailand |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2561 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 45 |
| Journal No. | 4 |
| Page no. | 308-311 |
| Keyword | Average absolute difference, Forecasting techniques, Minimum absolute difference, Simple statistical method, Small dataset |
| URL Website | https://www.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| 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. |
