Articles
| Open Access | OPTIMIZING TIME SERIES MODELING: SUBSET ARMA IDENTIFICATION FOR ENHANCED ANALYSIS OF MONTHLY ELECTRICITY CONSUMPTION DATA
Sayed Mubarak , Department of Applied Statistics and Insurance, Faculty of Commerce, Damietta University, EgyptAbstract
This study introduces an advanced approach to time series modeling through the application of Subset ARMA (AutoRegressive Moving Average) identification. Focused on monthly electricity consumption data, the research aims to refine and optimize the modeling process for improved forecasting accuracy. The Subset ARMA methodology allows for a more nuanced analysis, capturing specific temporal patterns within the dataset. The study explores the application of this technique, showcasing its enhanced capabilities in unraveling complex dependencies inherent in electricity consumption data. The findings contribute to the advancement of time series analysis methodologies and offer valuable insights for optimizing forecasting models in the energy sector.
Keywords
Time Series Analysis, Subset ARMA, AutoRegressive Moving Average
References
Baragona, R., F. Battaglia and D. Cucina, 2004. Estimatingthreshold subset autoregressive moving- average models bygenetic algorithms. Int. J. Stat., 62: 39-61.
Brockwell, P.J. and R.A. Davis, 1996. Introduction to Time Seriesand Forecasting. 3rd Edn., Springer, New York, ISBN-10: 0387947191.
Chatterjee, S., M. Laudato and L.A. Lynch, 1996. Geneticalgorithms and their statistical applications: An introduction. Comput. Stat. Data Anal., 22: 633-51.
Chen, J. andZ. Chen, 2008. Extended Bayesian information criteria formodel selection with large model space. Biometrika, 95: 759-771.
Durbin, J., 1960. The fitting of time series models. Rev. Int. Stat. Inst., 28: 233-243.
Gaetan, C., 1998. Subset ARMA model identification using genetic algorithms. J. Time Series Anal., 21: 559-570.
Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. 1st Edn.,Addison-Wesley, Reading, MA., ISBN-10: 0201157675, pp: 432.
Goldberg, D.E., 1990. A note on Boltzmann tournament selection for Genetic algorithms and population- oriented simulated annealing. Complex Syst., 4: 445-60.
Haggan, V. and O.B. Oyetunji, 1984. On the selection of subset autoregressive time series models. J. Time Series Anal., 5: 103-13.
Hannan, E.J. and J. Rissanen, 1982. Recursive estimation of mixed autoregressive-moving average order. Biometrika, 69: 81-94.
Mcleod, A.L. and Y. Zhang, 2006. Partial autocorrelation parameterization for subset autoregression. J. Time Series Anal., 27: 599-612.
Yu, G. and Y. Lin, 1991. A methodology for selecting subset autoregressive time series models. J. Time Series Anal., 12: 363-73.
Zhang, X. and R.D. Terrell, 1997. Projection modulus: A new direction for selecting subset autoregressive models. J. Time Series Anal., 18: 195-212.