Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/6441
Title: Sales Forecasting in Retail Industry Based on Dynamic Regression Models
Authors: Jorge Pinho de Sousa
José Manuel Oliveira
Patrícia Ramos
Issue Date: 2016
Abstract: Sales forecasts gained more importance in the retail industry with the increasing of promotional activity, not only because of the considerable portion of products under promotion but also due to the existence of promotional activities, which boost product sales and make forecasts more difficult to obtain. This study is performed with real data from a Portuguese consumer goods retail company, from January 2012 until April 2015. To achieve the purpose of the study, dynamic regression is used based on information of the focal product and its competitors, with seasonality modelled using Fourier terms. The selection of variables to be included in the model is done based on the lowest value of AIC in the train period. The forecasts are obtained for a test period of 30 weeks. The forecasting models overall performance is analyzed for the full period and for the periods with and without promotions. The results show that our proposed dynamic regression models with price and promotional information of the focal product generate substantially more accurate forecasts than pure time series models for all periods studied.
URI: http://repositorio.inesctec.pt/handle/123456789/6441
http://dx.doi.org/10.3233/978-1-61499-668-2-483
metadata.dc.type: conferenceObject
Publication
Appears in Collections:CESE - Articles in International Conferences
CTM - Articles in International Conferences

Files in This Item:
File Description SizeFormat 
P-00M-3MK.pdf1.54 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.