Sales Prediction With Machine Learning

Sales prediction with machine learning

Abstract:

This paper presents a use case of machine learning for sales forecasting in retail demand and sales prediction. In particular, the Auto Arima algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the Auto Arima predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the Auto Arima algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthens the sales prediction model.