Exponential smoothing constant determination to minimize the forecast error
One of the fundamental issues in exponential smoothing is to determine the smoothing constants. Researchers usually use the determination available in the statistical software. However, the result may not able to minimize the forecast error. For this study, the optimal values of smoothing constant a...
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| Format: | Article |
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Lviv Polytechnic National University
2022
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| _version_ | 1811137581689077760 |
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| author | Noor, N. A. M. Rahman, N. H. A. |
| author_facet | Noor, N. A. M. Rahman, N. H. A. |
| author_sort | Noor, N. A. M. |
| collection | UPM |
| description | One of the fundamental issues in exponential smoothing is to determine the smoothing constants. Researchers usually use the determination available in the statistical software. However, the result may not able to minimize the forecast error. For this study, the optimal values of smoothing constant are based on minimizing the forecast errors, mean absolute percentage error (MAPE) and root mean squared error (RMSE). The double exponential smoothing method or Holt's method is chosen where two constant values must identify specifically the level and trend estimate, respectively. The real data set of tourism emphasize the number of international tourists visit Malacca from year 2003 to 2016 has been studied. The result shows that the values of level and trend obtained from this analysis is small and close to zero. This indicates that the level and trend react slowly towards the data. In addition, simulation also have been computed using the random walk model. The result suggested, by using optimal result available by statistical software is not recommended since the obtained smoothing constants do not minimize the forecast error. |
| first_indexed | 2024-09-25T03:36:35Z |
| format | Article |
| id | upm.eprints-101428 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:36:35Z |
| publishDate | 2022 |
| publisher | Lviv Polytechnic National University |
| record_format | dspace |
| spelling | upm.eprints-1014282024-04-30T06:57:14Z http://psasir.upm.edu.my/id/eprint/101428/ Exponential smoothing constant determination to minimize the forecast error Noor, N. A. M. Rahman, N. H. A. One of the fundamental issues in exponential smoothing is to determine the smoothing constants. Researchers usually use the determination available in the statistical software. However, the result may not able to minimize the forecast error. For this study, the optimal values of smoothing constant are based on minimizing the forecast errors, mean absolute percentage error (MAPE) and root mean squared error (RMSE). The double exponential smoothing method or Holt's method is chosen where two constant values must identify specifically the level and trend estimate, respectively. The real data set of tourism emphasize the number of international tourists visit Malacca from year 2003 to 2016 has been studied. The result shows that the values of level and trend obtained from this analysis is small and close to zero. This indicates that the level and trend react slowly towards the data. In addition, simulation also have been computed using the random walk model. The result suggested, by using optimal result available by statistical software is not recommended since the obtained smoothing constants do not minimize the forecast error. Lviv Polytechnic National University 2022 Article PeerReviewed Noor, N. A. M. and Rahman, N. H. A. (2022) Exponential smoothing constant determination to minimize the forecast error. Lviv Polytechnic National University, 9 (1). 50 - 56. ISSN 2312-9794; ESSN: 2415-3788 https://science.lpnu.ua/mmc/all-volumes-and-issues/volume-9-number-1-2022/exponential-smoothing-constant-determination 10.23939/mmc2022.01.050 |
| spellingShingle | Noor, N. A. M. Rahman, N. H. A. Exponential smoothing constant determination to minimize the forecast error |
| title | Exponential smoothing constant determination to minimize the forecast error |
| title_full | Exponential smoothing constant determination to minimize the forecast error |
| title_fullStr | Exponential smoothing constant determination to minimize the forecast error |
| title_full_unstemmed | Exponential smoothing constant determination to minimize the forecast error |
| title_short | Exponential smoothing constant determination to minimize the forecast error |
| title_sort | exponential smoothing constant determination to minimize the forecast error |
| work_keys_str_mv | AT noornam exponentialsmoothingconstantdeterminationtominimizetheforecasterror AT rahmannha exponentialsmoothingconstantdeterminationtominimizetheforecasterror |