Forecasting promotional sales within the neighbourhood

Aguilar Palacios, Carlos and Muñoz Romero, Sergio and Rojo Álvarez, José Luis (2019). Forecasting promotional sales within the neighbourhood. "Ieee Access", v. 7 ; pp. 74759-74775. ISSN 2169-3536. https://doi.org/10.1109/ACCESS.2019.2920380.

Description

Title: Forecasting promotional sales within the neighbourhood
Author/s:
  • Aguilar Palacios, Carlos
  • Muñoz Romero, Sergio
  • Rojo Álvarez, José Luis
Item Type: Article
Título de Revista/Publicación: Ieee Access
Date: 2019
ISSN: 2169-3536
Volume: 7
Subjects:
Freetext Keywords: Automated feature selection, Weighted k-nearest neighbors, Online learning, Predictive models, Promotions, Supply chain, Non-negative least squares
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Promotions are a widely used strategy to engage consumers and as such, retailers dedicate immense effort and resources to their planning and forecasting. This paper introduces a novel interpretable machine learning method specifically tailored to the automatic prediction of promotional sales in real-market applications. Particularly, we present a fully-automated weighted k-nearest neighbours where the distances are calculated based on a feature selection process that focuses on the similarity of promotional sales. The method learns online, thereby avoiding the model being retrained and redeployed. It is robust and able to infer the mechanisms leading to sales as demonstrated on detailed surrogate models. Also, to validate this method, real market data provided by a worldwide retailer have been used, covering numerous categories from three different countries and several types of stores. The algorithm is benchmarked against an ensemble of regression trees and the forecast provided by the retailer and it outperforms both on a merit figure composed, not only by the mean absolute error, but also by the error deviations used in retail business. The proposed method significantly improves the accuracy of the forecast in many diverse categories and geographical locations, yielding significant and operative benefits for supply chains. Additionally, we briefly discuss in the Appendix how to deploy our method as a RESTful service in a production environment.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2016-75161-C2-1-RFINALEUniversidad Rey Juan CarlosInvestigación traslacional y transferencia de un nuevo sistema de electrofisiología cardiaca no inavasiva de alta resolución
Government of SpainTEC2016-81900-REDTKERMESUniversidad de ValenciaAvances en métodos núcleo para datos estructurados

More information

Item ID: 67130
DC Identifier: https://oa.upm.es/67130/
OAI Identifier: oai:oa.upm.es:67130
DOI: 10.1109/ACCESS.2019.2920380
Official URL: https://ieeexplore.ieee.org/document/8727882
Deposited by: Memoria Investigacion
Deposited on: 19 May 2021 06:57
Last Modified: 19 May 2021 06:57
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