K-th Moving, Weighted and Exponential Moving Average for Time Series Forecasting Models

Authors

  • Chris P. Tsokos

Keywords:

Time series, ARIMA, k-th moving average, k-th weighted moving average, k-th exponential weighted moving average process

Abstract

The objective of the present study is to investigate the effectiveness of developing a forecasting model of a given nonstationary economic realization using a k-th moving average, a k-th weighted moving average and a k-th exponential weighted moving average process. We create a new nonstationary time series from the original realization using the three different weighted methods. Using real economic data we formulate the best ARIMA model and compare short term forecasting results of the three proposed models with that of the classical ARIMA model.

Author Biography

  • Chris P. Tsokos

    Chris P. Tsokos, Ph.D.

    Distinguished University Professor-USF

    Mathematics & Statistics

    President of IFNA

    Executive Director of USOP

    Chief-Editor, Probability & Statistics, Atlantis Press

     

    Department of Mathematics & Statistics

    University of South Florida

    4202 East Fowler Ave

    Tampa, FL 33620-5700

    Tel:(813) 974-9734

    Fax:(813) 974-2700

    E-mail: [email protected]

     

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Published

2010-05-22

Issue

Vol. 3 No. 3 (2010): Special Issue on Granger Econometrics and Statistical Modeling

Section

Special Issue on Granger Econometrics and Statistical Modeling

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How to Cite

K-th Moving, Weighted and Exponential Moving Average for Time Series Forecasting Models. (2010). European Journal of Pure and Applied Mathematics, 3(3), 406-416. https://www.ejpam.com/index.php/ejpam/article/view/633