Abstract
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).
References
Alahi, A., Ramanathan, V., Fei-Fei, L.: Socially-aware large-scale crowd forecasting. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2211–2218. IEEE (2014)
Cortez, P.: Data mining with neural networks and support vector machines using the r/rminer tool. In: Perner, P. (ed.) ICDM 2010. LNCS (LNAI), vol. 6171, pp. 572–583. Springer, Heidelberg (2010). doi:10.1007/978-3-642-14400-4_44
Cortez, P.: Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, pp. 3694–3701. IEEE, July 2010
Cortez, P., Embrechts, M.J.: Using sensitivity analysis and visualization techniques to open black box data mining models. Inf. Sci. 225, 1–17 (2013)
Cortez, P., Rio, M., Rocha, M., Sousa, P.: Internet traffic forecasting using neural networks. In: Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN 2006), Vancouver, Canada, pp. 4942–4949. IEEE, July 2006
Diebold, F.X., Mariano, R.S.: Comparing predictive accuracy. J. Bus. Econ. Stat. 13, 253–263 (2012)
Donate, J.P., Cortez, P., Sánchez, G.G., De Miguel, A.S.: Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble. Neurocomputing 109, 27–32 (2013)
Duque, D., Santos, H., Cortez, P.: Prediction of abnormal behaviors for intelligent video surveillance systems. In: CIDM, pp. 362–367. IEEE (2007)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Inf. Retrieval 4(2), 133–151 (2001)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)
Hyndman, R., Khandakar, Y.: Automatic time series forecasting: the forecast package for r 7, 2008 (2007). http://www.jstatsoft.org/v27/i03
Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M.: Activity forecasting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 201–214. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33765-9_15
Ma, S., Fildes, R., Huang, T.: Demand forecasting with high dimensional data: the case of sku retail sales forecasting with intra- and inter-category promotional information. Eur. J. Oper. Res. 249(1), 245–257 (2016)
Makridakis, S., Weelwright, S., Hyndman, R.: Forecasting: Methods and Applications, 3rd edn. Wiley, New York (1998)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2015)
Segetlija, Z., et al.: New approaches to the modern retail management. Interdisc. Manage. Res. 5, 177–184 (2009)
Stepnicka, M., Cortez, P., Donate, J.P., Stepnicková, L.: Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations. Expert Syst. Appl. 40(6), 1981–1992 (2013)
Tashman, L.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. Forecast. J. 16(4), 437–450 (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I-511. IEEE (2001)
Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, San Franscico (2011)
Acknowledgments
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
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Cortez, P., Matos, L.M., Pereira, P.J., Santos, N., Duque, D. (2017). Forecasting Store Foot Traffic Using Facial Recognition, Time Series and Support Vector Machines. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_26
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