Accuracy of the Moving Averages and Deseasonalizing Methods for Trend, Cyclical and Seasonal Data Forecasting

Abstract

Forecasting or forecasting is an attempt to predict future conditions based on past state data. Moving Averages or moving average is a forecasting method that calculates the average value of a time series and then uses it to estimate the value in the next period. Deseasonalizing is part of the decomposition method which is included in the time series method. In this study, the Moving Average method and the Deseasonalizing method were used. The use of these two forecasting methods is to determine the accuracy of the forecasting method which is more accurate and close to the Mean Absolute Error (MAE) and Mean Squared Error (MSE) values. In this study the procedures used were problem identification, problem formulation, observation, data analysis and conclusion. The data taken in this study is data that contains trend, cyclical, and seasonal. For data containing trends on the moving averages method 15245.28 and 1430419308, for the Deseasonalizing method 28121.9504 and 1204814887. For Cyclical data on the Moving Averages method 4454.314465 and 28200197.22 for the Deseasonalizing method 13357.71283 and 254833253.4. For Seasonal data on Moving Averages 126.3839286 and 25479.38393 for the Deseasonalizing method 244.9971767 and 75372.32397. And for data containing these three patterns in the Moving Averages method 193.5385 and 65781.02 for the Deseasonalizing method 901.9566 and 1351418. From these results it can be concluded that the most effective trend data is the Deseasonalizing method, for Seasonal data the most effective method is the Moving Averages method, and for Cyclical Data the most effective method is the Moving Averages. Meanwhile, for data containing the three data patterns is the Moving Averages method.