An Optimization of the Autoregressive Model Using the Grid Search Method

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The purpose of this study is to find the parameters that can produce the best value on the model Autoregressive (AR). The parameter evaluation method used is the Maximum Likelihood Estimator (MLE) and using Grid Search optimization methods. The experimental data used in this study was a sunspot dataset. Based on our analysis, the best Autoregressive model was a 3rd order AR model.

     

     


  • Keywords


    Numerical optimization; Autoregressive model; MLE; Grid search method.

  • References


    1. [1] C. Chatfield. (2002). Time-Series Forecasting.

      [2] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel. (2008). Time Series Analysis Forecasting and Control 4th Edition.

      [3] H. M. ALBEHADILI, ABDURRAHMAN, and N. E. ISLAM, "An Algorithm for Time Series Prediction Using Particle Swarm Optimization (PSO) " International Journal of Scientific Knowledge, vol. 4 No. 6, pp. 26-33, 2014.

      [4] R. Adhikari and R. K. Agrawal. (2008). An Introductory Study on Time Series Modeling and Forecasting.

      [5] R. Adhikari, R. K. Agrawal, and L. Kant, "PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting," in 2013 3rd IEEE Int. Adv. Comput. Conf., 2013, pp. 719–725.

      [6] M. Sheikhalishahi, V. Ebrahimipour, H. Shiri, H. Zaman, and M. Jeihoonian, "A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem," Int. J. Adv. Manuf. Technol., vol. 68, pp. 317–338, 2013.

      [7] Haviluddin, R. Alfred, J. H. Obit, M. H. A. Hijazi, and A. A. A. Ibrahim, "A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data," Advanced Science Letters, pp. 3037-3041, 2015.

      [8] A. S. Ahmar, "A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia," International Journal of Engineering & Technology, vol. 7, pp. 9-11, 2018.

      [9] A. S. Ahmar, S. Guritno, A. Rahman, I. Minggi, M. Arif Tiro, M. Kasim Aidid, et al., "Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)," Journal of Physics: Conf. Series, vol. 954, 2018.

      [10] A. Rahman and A. S. Ahmar, "Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models," in AIP Conference Proceedings vol. 1885, ed, 2017.

      [11] S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting Methods and Applications, 3rd Edition, 3rd Edition ed. New York: John Wiley, New York, 1998.

      [12] R. Fildes and S. Makridakis, "The Impact of Empirical Accuracy Studies on Time Series Analysis and Forecasting," Int. Stat. Rev. / Rev. Int. Stat., vol. 63, pp. 289–308, 1995.

      [13] Haviluddin and A. Jawahir, "Comparing of ARIMA and RBFNN for short-term forecasting," International Journal of Advances on Intelligent Informatics (IJAIN), vol. 1, (1), March 2015, pp. 15-22, 2015.


 

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Article ID: 12739
 
DOI: 10.14419/ijet.v7i2.2.12739




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