In this paper, we discuss three analytical time series models for selecting the more effective with an accurate forecasting models, among others. We analytically modify the stochastic realization utilizing (i) k-th moving average, (ii) k-th weighted moving average, and (iii) k-th exponential weighted moving average processes. The examining methods have been applied for 1000 independent datasets for five different parameters with possible orders . We consider stationary data , and non-stationary data with first and second differences for ARIMA models. We consider short term and long term, observations. A similar forecasting models was developed and evaluated for the daily closing price of Stock Price of the PALTEL company in Palestine. The main finding is that, in most simulated datasets one or more of the proposed models give better forecast accuracy than the classical model (ARIMA). Specially, in most simulated datasets 3– time Exponential Weighted Moving Average based on Autoregressive Integrated Moving Average (EWMA3-ARIMA) is the best forecasting model among all other models. For PALTEL Stock Price, the best forecasting model is 3–time Moving Average based on Autoregressive Integrated Moving Average (MA3-ARIMA) among all other models.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 2, Issue 6) |
| DOI | 10.11648/j.ajtas.20130206.17 |
| Page(s) | 202-209 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Moving Average, Weighted Moving Average, Exponential Weighted Moving Average, Stationary, Forecasting Accuracy, ARIMA Models
| [1] | Armstrong, J. (2001). Principles of forecasting: a handbook for researchers and practitioners. Norwell, Massachusetts: Kluwer Academic Publishers. |
| [2] | Armstrong, J., and Collopy, F. (1992). Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons. International Journal of Forecasting, Vol. 8(1), pages 69-80. |
| [3] | Billah, B., King, M. L., Snyder, R., and Koehler, A. (2005). Exponential Smoothing Model Selection for Forecasting, Australia. Monash University. Department of Econometrics and Business Statistics. |
| [4] | Box, G., Jenkins, G., and Reinsel, G. (1994). Time series Analysis Forecasting and Control, Third edition. Prentice Hall, Englewood Gliffs, NJ. |
| [5] | Brockwell, P., and Davis, R. (1996). Introduction to Time Series and Forecasting. Springer, New York. |
| [6] | Crane, D. and Eratly, J. (1967). A two stage forecasting model: exponential smoothing and multiple regression, Management Science, Vol. 13(8). |
| [7] | Cryer, J. and Chan, K. (2008). Time Series Analysis with Applications in R. Springer, New York. |
| [8] | Harvey, A.C., (1993). Time Series Models, Second edition. Harvester Wheat sheaf. |
| [9] | Hyndman, R., and Koehler, A. (2005). Another Look at measures of forecast accuracy. Australia, Monash University, Department of Econometrics and Business Statistics. |
| [10] | Parthasarath, P., and Levinson, D. (2010). Post – Construction evaluation of traffic forecast accuracy. Journal of Transport Policy. |
| [11] | Shami, R., and Snyder, R. (1998). Exponential smoothing methods of forecasting and general ARMA time series representations. Monash University, Business and Economics. |
| [12] | Shih, S. and Tsokos, C. (2008). A weighted Moving Average Process for Forecasting. Journal of Modern Applied Statistical Methods. |
| [13] | Shumway, R., and Stoffer, D. (2006). Time Series Analysis and Its Applications: with R Examples, Second edition., Springer, New York. |
| [14] | Steiner, S. (1996). Grouped Data Exponentially Weighted Moving Average Control Charts. University of Waterloo, Canada. |
| [15] | Trigg, D. and Leach, A. (1967). Exponential smoothing with an adaptive response rate. Operations Research Quarterly, Vol. 18(1). |
| [16] | Tsokos, C. (2010). K-th Moving, Weighted and Exponential Moving Average for Time Series Forecasting Models. European Journal of Pure and Applied Mathematics, Vol. 3(3), pages 406-416. |
| [17] | Wei, W. (2006). Time Series Analysis Univariate and Multivariate Methods, Second edition, Pearson Education, Inc. |
APA Style
Samir K. Safi, Issam A. Dawoud. (2013). Comparative Study on Forecasting Accuracy among Moving Average Models with Simulation and PALTEL Stock Market Data in Palestine. American Journal of Theoretical and Applied Statistics, 2(6), 202-209. https://doi.org/10.11648/j.ajtas.20130206.17
ACS Style
Samir K. Safi; Issam A. Dawoud. Comparative Study on Forecasting Accuracy among Moving Average Models with Simulation and PALTEL Stock Market Data in Palestine. Am. J. Theor. Appl. Stat. 2013, 2(6), 202-209. doi: 10.11648/j.ajtas.20130206.17
AMA Style
Samir K. Safi, Issam A. Dawoud. Comparative Study on Forecasting Accuracy among Moving Average Models with Simulation and PALTEL Stock Market Data in Palestine. Am J Theor Appl Stat. 2013;2(6):202-209. doi: 10.11648/j.ajtas.20130206.17
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Download@article{10.11648/j.ajtas.20130206.17,
author = {Samir K. Safi and Issam A. Dawoud},
title = {Comparative Study on Forecasting Accuracy among Moving Average Models with Simulation and PALTEL Stock Market Data in Palestine},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {2},
number = {6},
pages = {202-209},
doi = {10.11648/j.ajtas.20130206.17},
url = {https://doi.org/10.11648/j.ajtas.20130206.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20130206.17},
abstract = {In this paper, we discuss three analytical time series models for selecting the more effective with an accurate forecasting models, among others. We analytically modify the stochastic realization utilizing (i) k-th moving average, (ii) k-th weighted moving average, and (iii) k-th exponential weighted moving average processes. The examining methods have been applied for 1000 independent datasets for five different parameters with possible orders . We consider stationary data , and non-stationary data with first and second differences for ARIMA models. We consider short term and long term, observations. A similar forecasting models was developed and evaluated for the daily closing price of Stock Price of the PALTEL company in Palestine. The main finding is that, in most simulated datasets one or more of the proposed models give better forecast accuracy than the classical model (ARIMA). Specially, in most simulated datasets 3– time Exponential Weighted Moving Average based on Autoregressive Integrated Moving Average (EWMA3-ARIMA) is the best forecasting model among all other models. For PALTEL Stock Price, the best forecasting model is 3–time Moving Average based on Autoregressive Integrated Moving Average (MA3-ARIMA) among all other models.},
year = {2013}
}
TY - JOUR T1 - Comparative Study on Forecasting Accuracy among Moving Average Models with Simulation and PALTEL Stock Market Data in Palestine AU - Samir K. Safi AU - Issam A. Dawoud Y1 - 2013/11/10 PY - 2013 N1 - https://doi.org/10.11648/j.ajtas.20130206.17 DO - 10.11648/j.ajtas.20130206.17 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 202 EP - 209 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20130206.17 AB - In this paper, we discuss three analytical time series models for selecting the more effective with an accurate forecasting models, among others. We analytically modify the stochastic realization utilizing (i) k-th moving average, (ii) k-th weighted moving average, and (iii) k-th exponential weighted moving average processes. The examining methods have been applied for 1000 independent datasets for five different parameters with possible orders . We consider stationary data , and non-stationary data with first and second differences for ARIMA models. We consider short term and long term, observations. A similar forecasting models was developed and evaluated for the daily closing price of Stock Price of the PALTEL company in Palestine. The main finding is that, in most simulated datasets one or more of the proposed models give better forecast accuracy than the classical model (ARIMA). Specially, in most simulated datasets 3– time Exponential Weighted Moving Average based on Autoregressive Integrated Moving Average (EWMA3-ARIMA) is the best forecasting model among all other models. For PALTEL Stock Price, the best forecasting model is 3–time Moving Average based on Autoregressive Integrated Moving Average (MA3-ARIMA) among all other models. VL - 2 IS - 6 ER -
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