Forecasting Store Foot Traffic Using Facial Recognition, Time Series and Support Vector Machines
-
Paulo Cortez [1] ; Luís Miguel Matos [1] ; Pedro José Pereira [1] ; Nuno Santos [3] ; Duarte Duque [2]
-
[1] Universidade do Minho
Universidade do Minho
Braga (São José de São Lázaro), Portugal
-
[2] Instituto Politécnico do Cávado e do Ave
Instituto Politécnico do Cávado e do Ave
Arcozelo, Portugal
-
[3] EXVA Technologies (Braga, Portugal)
-
- Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 267-278
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
-
Resumen
In this paper, we explore data collected in a pilot project that used a digital camera and facial recognition to detect foot traffic to a sports store. Using a time series approach, we model daily incoming store traffic under three classes (all faces, female, male) and compare six forecasting approaches, including Holt-Winters (HW), a Support Vector Machine (SVM) and a HW-SVM hybrid that includes other data features (e.g., weather conditions). Several experiments were held, under a robust rolling windows scheme that considers up to one week ahead predictions and two metrics (predictive error and estimated store benefit). Overall, competitive results were achieved by the SVM (all faces), HW (female)and HW-SVM (male) methods, which can potentially lead to valuable gains (e.g., enhancing store marketing or human resource management).
