Comparison of Time Series ARIMA Model and Support Vector Regression
AUTHORS
Yekta S. Amirkhalili,School of Industrial & Systems Engineering, College of Engineering, University of Tehran, Iran
Amir Aghsami,School of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Fariborz Jolai,School of Industrial & Systems Engineering, College of Engineering, University of Tehran, Iran
ABSTRACT
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 relies 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 modelling with respect 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 is compared. The results obtained from our analysis shows 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.
KEYWORDS
Time series analytics, Support vector regression, Sales forecasting
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CITATION
COPYRIGHT
© 2020 Yekta S. Amirkhalili et al. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.