Comparison of Time Series ARIMA Model and Support Vector Regression

As one of the most important and costly functions of any business, sales analytics has been the target of many studies for some time now. Knowing and tracking the sales of a business proves useful in all data-driven decisions made from inventory management to shelf layouts in a supermarket. However, forecasting sales rely heavily on data and algorithms strong enough to handle unseen data. Since sales data are in nature time series datasets one of such predictive methods is time series analytics. In this paper, the ARIMA modeling to the seasonality of the data is compared with a machine learning technique, support vector regression. These comparisons are carried out on three different and unrelated datasets and these algorithms' errors when predicting future sales are compared. The results obtained from our analysis show poor results in general due to datasets having large numbers of oscillation and outliers, but for comparison purposes these datasets and results are fine. We conclude that support vector regression produces better results in comparison with time series analytics on all datasets used in this paper.