Abstract
This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of ARIMA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ediger, V.Ş., Akar, S.: ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35, 1701–1708 (2007)
Erdogdu, E.: Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy Policy 35, 1129–1146 (2007)
Ong, C.-S., Huang, J.-J., Tzeng, G.-H.: Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation 164, 885–912 (2005)
Niu, D., Cao, S., Zhao, L., Zhang, W.: Power load forecasting technology and its applications. China Electric Power Press, Beijing (1998) (in Chinese)
Ediger, V.Ş., Akar, S., Uğurlu, B.: Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model. Energy Policy 34, 3836–3846 (2006)
Valenzuela, O., Rojas, I., Rojas, F., Pomares, H., Herrera, L.J., Guillen, A., Marquez, L., Pasadas, M.: Hybridization of intelligent techniques and ARIMA models for time series prediction. Fuzzy Sets and Systems 159, 821–845 (2008)
Li, M., Zhou, J.-z., Li, J.-p., Liang, J.-w.: Predicting Securities Market in Shanghai and Shenzhen by ARMA Model. Journal of Changsha Railway University 3, 78–84 (2000) (in Chinese)
Dingxue, Z., Xinzhi, L., Zhihong, G.: A Dynamic Clustering Algorithm Based on PSO and Its Application in Fuzzy Identification. In: The 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 232–235. IEEE Press, New York (2006)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: The IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Press, New York (1995)
Alatas, B., Akin, E., Bedri Ozer, A.: Chaos embedded particle swarm optimization algorithms. Chaos, Solitons and Fractals (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Zhao, W. (2009). ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-05253-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05252-1
Online ISBN: 978-3-642-05253-8
eBook Packages: Computer ScienceComputer Science (R0)