The m-delay Autoregressive Model with Application
The classical autoregressive (AR) model has been widely applied to predict future data using m past observations over five decades. As the classical AR model required m unknown parameters, this paper implements the AR model by reducing m parameters to two parameters to obtain a new model with an opt...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
Tech Science Press
2020
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| Subjects: | |
| Online Access: | https://www.techscience.com/CMES/v122n2/38310 http://hdl.handle.net/20.500.11937/79257 |
| _version_ | 1848764023734009856 |
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| author | Ratchagit, Manlika Wiwatanapataphee, Benchawan Dokuchaev, Nikolai |
| author_facet | Ratchagit, Manlika Wiwatanapataphee, Benchawan Dokuchaev, Nikolai |
| author_sort | Ratchagit, Manlika |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The classical autoregressive (AR) model has been widely applied to predict future data using m past observations over five decades. As the classical AR model required m unknown parameters, this paper implements the AR model by reducing m parameters to two parameters to obtain a new model with an optimal delay called as the m-delay AR model. We derive the m-delay AR formula for approximating two unknown parameters based on the least squares method and develop an algorithm to determine optimal delay based on a brute-force technique. The performance of the m-delay AR model was tested by comparing with the classical AR model. The results, obtained from Monte Carlo simulation using the monthly mean minimum temperature in Perth Western Australia from the Bureau of Meteorology, are no significant difference compared to those obtained from the classical AR model. This confirms that the m-delay AR model is an effective model for time series analysis. |
| first_indexed | 2025-11-14T11:12:46Z |
| format | Journal Article |
| id | curtin-20.500.11937-79257 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:12:46Z |
| publishDate | 2020 |
| publisher | Tech Science Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-792572020-08-03T05:29:31Z The m-delay Autoregressive Model with Application Ratchagit, Manlika Wiwatanapataphee, Benchawan Dokuchaev, Nikolai Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Mathematics, Interdisciplinary Applications Engineering Mathematics Delay autoregressive model least squares method brute-force technique ORDER SELECTION SERIES CRITERIA The classical autoregressive (AR) model has been widely applied to predict future data using m past observations over five decades. As the classical AR model required m unknown parameters, this paper implements the AR model by reducing m parameters to two parameters to obtain a new model with an optimal delay called as the m-delay AR model. We derive the m-delay AR formula for approximating two unknown parameters based on the least squares method and develop an algorithm to determine optimal delay based on a brute-force technique. The performance of the m-delay AR model was tested by comparing with the classical AR model. The results, obtained from Monte Carlo simulation using the monthly mean minimum temperature in Perth Western Australia from the Bureau of Meteorology, are no significant difference compared to those obtained from the classical AR model. This confirms that the m-delay AR model is an effective model for time series analysis. 2020 Journal Article http://hdl.handle.net/20.500.11937/79257 10.32604/cmes.2020.08865 English https://www.techscience.com/CMES/v122n2/38310 http://creativecommons.org/licenses/by-nc-nd/4.0/ Tech Science Press unknown |
| spellingShingle | Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Mathematics, Interdisciplinary Applications Engineering Mathematics Delay autoregressive model least squares method brute-force technique ORDER SELECTION SERIES CRITERIA Ratchagit, Manlika Wiwatanapataphee, Benchawan Dokuchaev, Nikolai The m-delay Autoregressive Model with Application |
| title | The m-delay Autoregressive Model with Application |
| title_full | The m-delay Autoregressive Model with Application |
| title_fullStr | The m-delay Autoregressive Model with Application |
| title_full_unstemmed | The m-delay Autoregressive Model with Application |
| title_short | The m-delay Autoregressive Model with Application |
| title_sort | m-delay autoregressive model with application |
| topic | Science & Technology Technology Physical Sciences Engineering, Multidisciplinary Mathematics, Interdisciplinary Applications Engineering Mathematics Delay autoregressive model least squares method brute-force technique ORDER SELECTION SERIES CRITERIA |
| url | https://www.techscience.com/CMES/v122n2/38310 http://hdl.handle.net/20.500.11937/79257 |