Employing the principal components in time series models and selecting the best models with Application
In this study, principal components analysis, which is one of the methods of multivariate analysis for prediction of time series models (Box-Jenkins Model) was used by applying to electric power data (Erbil Gas Power Plant) (EGPS) which contains multivariate data (5 stations) and the data was month...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Salahaddin University-Erbil
2023-04-01
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| Series: | Zanco Journal of Humanity Sciences |
| Online Access: | https://zancojournal.su.edu.krd/index.php/JAHS/article/view/552 |
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| Summary: | In this study, principal components analysis, which is one of the methods of multivariate analysis for prediction of time series models (Box-Jenkins Model) was used by applying to electric power data (Erbil Gas Power Plant) (EGPS) which contains multivariate data (5 stations) and the data was monthly for the period from (1/1/2017) to (14/9/2021).
The idea of the research was based on applying principal component analysis to multiple time series data, obtaining the components extracted from them, and then estimating the Box-Jenkins Models.
The main conclusion is that principal component analysis is effective in reducing multiple time series data and obtaining the best models based on statistical criteria.
And finally, the best proposed model for predicting electrical energy production data in the City of Erbil is (ARIMA(2,2,2)x(2,2,0)12).
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| ISSN: | 2412-396X |