Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting

Year 2019, Volume: 7 Issue: 1, 20 - 26, 31.01.2019

Cagatay Catal , Kaan Ece Begum Arslan Akhan Akbulut

https://doi.org/10.17694/bajece.494920

Cited By: 19

Abstract

Predicting the sales amount as close as to the
actual sales amount can provide many benefits to companies. Since the fashion
industry is not easily predictable, it is not straightforward to make an
accurate prediction of sales.  In this
study, we applied not only regression methods in machine learning, but also
time series analysis techniques to forecast the sales amount based on several
features. We applied our models on Walmart sales data in Microsoft Azure
Machine Learning Studio platform. The following regression techniques were
applied: Linear Regression, Bayesian Regression, Neural Network Regression,
Decision Forest Regression and Boosted Decision Tree Regression. In addition to
these regression techniques, the following time series analysis methods were implemented:
Seasonal ARIMA, Non-Seasonal ARIMA, Seasonal ETS, Non -Seasonal ETS, Naive
Method, Average Method and Drift Method. It was shown that Boosted Decision Tree
Regression provides the best performance on this sales data. This project is a
part of the development of a new decision support system for the retail
industry.

Keywords

Sales forecasting, regression, machine learning, time series analysis

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