Prediction For the sale of Diyala Electric Industries using time series after excluding the impact of the season of the series
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Abstract
The topic of time series is one of the important statistical methods used to predict a number of phenomena that change over time in many economic, administrative, agricultural, commercial fields, etc.
The main focus of this study is to study the effect of many factors that influence the time series, the most important of which is the seasonal change in the time series in terms of the focus the research to eliminate the effect of the season and then predict the future of the phenomenon to predict the time series in order to exclude the effect of the season.
Two statistical methods were used in this study (the method of ratio to the general average) where both methods showed the same results in case of excluding the effect of the season and gave the results of the prediction itself, this indicates the preference of the two methods to predict the case of the time series data affected by the season as data under studying. This study proved that the sales of the company’s products lead to an increase in its profits which are all of MI3 (20-40) and MI1 (10-40) because they gave good previous profits to the company and a significant future forecasts for the company's sales. There are some sales from company’s products have given fluctuating profits during seasons that are not constant but vary from one season to another (30-90), electric heater, Arcon gas, 33400, 1600 and 11/100). These products should be given some importance for the purpose of eliminating the fluctuation in sales. Other products of the company's products are new sales, During the last year or two up to date, causing not to give them future predictive values due to the final cessation of both (Mkni 1 (10-40) .The results of the study gave the following predictive values after deducting the effect of the season on the company's products sales in order to know the most important profits for future use.
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