Modeling daily guest count prediction

Ricky Fok; Agnieszka Lasek; Jiye Li; Aijun An; Ricky Fok; Agnieszka Lasek; Jiye Li; Aijun An

doi:10.3934/bdia.2016012

Big Data and Information Analytics

2016, Volume 1, Issue 4: 299-308. doi: 10.3934/bdia.2016012

Previous Article Next Article

Modeling daily guest count prediction

Department of Computer Science and Engineering York University 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada

Published: 01 October 2016

We present a novel method for analyzing data with temporal variations. In particular, the problem of modeling daily guest count forecast for a restaurant with more than 60 chain stores is presented. We study the transaction data collected from each store, perform data preprocessing and feature constructions for the data. We then discuss different forecasting techniques based on data mining and machine learning techniques. A new modeling algorithm SW-LAR-LASSO is proposed. We compare multiple regression model, poisson regression model, and the proposed SW-LAR-LASSO model for prediction. Experimental results show that the approach of combining sliding windows and LAR-LASSO produces the best results with the highest precision. This approach can also be applied to other areas where temporal variations exist in the data.
- Guest count prediction,
- multiple regression,
- poisson regression,
- least angle regression
Citation: Ricky Fok, Agnieszka Lasek, Jiye Li, Aijun An. Modeling daily guest count prediction[J]. Big Data and Information Analytics, 2016, 1(4): 299-308. doi: 10.3934/bdia.2016012

Related Papers:

Abstract

We present a novel method for analyzing data with temporal variations. In particular, the problem of modeling daily guest count forecast for a restaurant with more than 60 chain stores is presented. We study the transaction data collected from each store, perform data preprocessing and feature constructions for the data. We then discuss different forecasting techniques based on data mining and machine learning techniques. A new modeling algorithm SW-LAR-LASSO is proposed. We compare multiple regression model, poisson regression model, and the proposed SW-LAR-LASSO model for prediction. Experimental results show that the approach of combining sliding windows and LAR-LASSO produces the best results with the highest precision. This approach can also be applied to other areas where temporal variations exist in the data.

References

[1]	[ S. Coxe, S. G. West and L. S. Aiken, The analysis of count data:A gentle introduction to poisson regression and its alternatives, J. Pers. Assess., 91(2009), 121-136.
[2]	[ B. Efron, T. Hastie, I. Johnstone and R. Tibshirani, Least angle regression, The Annals of Statistics, 32(2004), 407-499.
[3]	[ F. G. Forst, Forecasting restaurant sales using multiple regression and box-jenkins analysis, J. Appl. Bus. Res., 8(1992), 2157-8834.
[4]	[ T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Springer Series in Statistics, Springer, New York, 2009.
[5]	[ S. E. Kimes, R. B. Chase, S. Choi, P. Y. Lee and E. N. Ngonzi, Restaurant revenue management applying yield management to the restaurant industry, Cornell Hospitality Q., 39(1998), 32-39.
[6]	[ A. Lasek, N. Cercone and J. Saunders, Restaurant sales and customer demand forecasting:Literature survey and categorization of methods, Smart City 360, 166(2016), 479-491.
[7]	[ M. S. Morgan and P. K. Chintagunta, Forecasting restaurant sales using self-selectivity models, J. Retail. Consum. Serv., 4(1997), 117-128.
[8]	[ D. Reynolds, I. Rahman and W. Balinbin, Econometric modeling of the U.S. restaurant industry International, J. Hospitality Manage., 34(2013), 317-323.
[9]	[ K. Ryu and A. Sanchez, The evaluation of forecasting methods at an institutional foodservice dining facility, J. Hospitality Financ. Manage., (2013), 27-45.
[10]	[ K. F. Sellers and G. Shmueli, Predicting censored count data with COM-Poisson regression, Working Paper, Indian School of Business, Hyderabad, 2010.
[11]	[ J. T. Wulu Jr., K. P. Singh, F. Famoye, T. N. Thomas and G. McGwin, Regression analysis of count data, J. Ind. Soc. Ag. Statistics, 55(2002), 220-231.

Reader Comments

Your name:*

Email:*
© 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)