Compare Prediction by Autoregressive Integrated Moving Average Model from first order with Exponential Weighted Moving Average

  • علي سلمان حبيب

Abstract

The prediction process of time series for some time-related phenomena, in particular, the autoregressive integrated moving average(ARIMA) models is one of the important topics in the theory of time series analysis in the applied statistics. Perhaps its importance lies in the basic stages in analyzing of the structure or modeling and the conditions that must be provided in the stochastic process. This paper deals with two methods of predicting the first was a special case of autoregressive integrated moving average which is ARIMA (0,1,1) if the value of the parameter equal to zero, then it is called Random Walk model, the second was the exponential weighted moving average (EWMA). It was implemented in the data of the monthly traffic accidents in the province of Dhi Qar Governorate for the period from (Jan. 2011) to (Aug. 2019). It was found through the research that the model studied is well of the traffic accident, we can predict dangerous traffic accident using this model and reduce the aggravation through Develop plans strategic of the roads.

Published
2020-06-30
How to Cite
حبيبع. (2020) “Compare Prediction by Autoregressive Integrated Moving Average Model from first order with Exponential Weighted Moving Average”, Journal of Economics and Administrative Sciences, 26(120), pp. 426-439. doi: 10.33095/jeas.v26i120.1925.
Section
Statistical Researches