Sales Forecast by Using Deep Rectifier Network

Oscar Chang, Galo Mosquera, Zenaida Castillo, Luis Zhinin-Vera

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


Sales forecast is a key issue in pharmacy wholesales and has a direct effect in inventory cost, it also represents a difficult problem, affected by factors like promotions, price changes and seasonal preferences. Traditional sales forecast techniques use historical sales data and recursive formulas, which limit their accuracy. More recently learning-based methods have expanded data features capture capabilities. This paper presents a deep architecture which explores a few years of weekly sales data and learns to makes assertive predictions. The system is assembled with ReLU neurons, whose learning behavior makes possible the effective training of sparse coded deep sub-nets. The developed learning algorithm uses two learning stages where the first produces sparse representation of the studied time series and the second harvest one week ahead predictions using this sparse data. In both cases the reward system is future-focused and favors search for future capacities. To achieving successful network training we develop an algorithms that deals with exploding gradient problem, typical of ReLU networks. The fully assembled predictor automatically learn features from structured data and produces inventories with improved dollar cost. The system has been tested in real time with real data.

Idioma originalInglés
Título de la publicación alojadaProceedings of the Future Technologies Conference, FTC 2020, Volume 1
EditoresKohei Arai, Supriya Kapoor, Rahul Bhatia
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas12
ISBN (versión impresa)9783030631277
EstadoPublicada - 2021
EventoFuture Technologies Conference, FTC 2020 - San Francisco, Estados Unidos
Duración: 5 nov. 20206 nov. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365


ConferenciaFuture Technologies Conference, FTC 2020
País/TerritorioEstados Unidos
CiudadSan Francisco


Profundice en los temas de investigación de 'Sales Forecast by Using Deep Rectifier Network'. En conjunto forman una huella única.

Citar esto