Employing the principal components in time series models and selecting the best models with Application
DOI:
https://doi.org/10.21271/zjhs.27.2.26Abstract
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).
References
• Abdul-Ahad, Manahil Daniel, (2004): “The immune estimation in the first-order autoregressive model”, Master’s thesis, College of Computer Science and Mathematics, University of Mosul.
• Bisgaard, S. and Kulahci, M., 2011. Time series analysis and forecasting by example. John Wiley & Sons.
• Dunteman, G.H., 1989. Principal components analysis (No. 69). Sage.
• Guo, J., Peng, Y., Peng, X., Chen, Q., Yu, J. and Dai, Y., 2009, August. Traffic forecasting for mobile networks with multiplicative seasonal ARIMA models. In 2009 9th International Conference on Electronic Measurement & Instruments (pp. 3-377). IEEE.
• KARAKAŞ, G., 2021. Forecasting of Natural Honey Yield in Turkey through ARIMA Model. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 38(3), pp.166-172.
• Ladalla, N., Josef (2000). "Multivariate Time Series Analaysis in Principal Components Space", The 32nd Symposium on interface: computing science and statistic, New Orleans, Louisiana.
• Miao, D., Qin, X. and Wang, W., 2014, October. The periodic data traffic modeling based on multiplicative seasonal ARIMA model. In 2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP) (pp. 1-5). IEEE.
• Smith, L.I., 2002. A tutorial on principal component.
• Tian, S., Fu, Y., Ling, P., Wei, S., Liu, S. and Li, K., 2018, November. Wind power forecasting based on arima-lgarch model. In 2018 International Conference on Power System Technology (POWERCON) (pp. 1285-1289). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Rizgar Maghded Ahmed, Nida Salim Mala Younis
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Except where otherwise noted, content on this site is licenced
under a Creative Commons Attribution License 4.0 (CC BY- 4.0)
