Comparison of Forecasting Accuracy Using the Short Moving Average (SMA) Method Using Boxplot Outlier Filtering and Not Using Outlier Filtering for Data that has a high level of variation

Abstract

The Short Moving Average (SMA) forecasting method is one of the most widely used forecasting methods, especially for processing data with a high level of variation and is not linear with time. However, opportunities to develop and improve forecasting performance using the SMA method are still wide open. The performance of a forecasting method can be seen from the distribution of errors. SMA does not see and does not sort the type of input data that will be processed into a forecast value, whether the input data has small or large variations, or has outlier data. If the input data has an outlier, then that outlier can make the forecasting performance not good. One of the efforts to improve SMA forecasting performance is by filtering outlier data. In this study, a comparison was made of the forecasting results for SMA using outlier filtering with the forecasting results for SMA not using outlier filtering. The next step is to compare the error values, namely those that produce the smallest Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. From the results of the study it can be seen that the performance of SMA using the Boxplot filtering method gives better forecasting results than those without using outlier filtering.