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Modeling daily guest count prediction

  • * Corresponding author: Ricky Fok

    * Corresponding author: Ricky Fok 
Abstract Full Text(HTML) Figure(3) / Table(1) Related Papers Cited by
  • 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.

    Mathematics Subject Classification: Primary:58F15, 58F17;Secondary:53C35.


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  • Figure 1.  Examples of boxplots for some of the stores from the chain of restaurants

    Figure 2.  Three iterations of the sliding window are shown. Each line interval denotes a week. The shaded boxes denote the sliding windows for the training data over eight weeks and the empty boxes denote the weeks where the guest counts are predicted

    Figure 3.  Experimental process for guest count predictions

    Table 1.  Table of results from chosen stores. The bolded results denote the lowest predictive error among the three algorithms tested

    Benchmark StoresMultiple regressionPoisson regressionSW-LAR-LASSOlocalization
    Store_1 7.888.288.40Canada stores
    Store_215.5616.71 15.00
    Store_3 10.2010.8610.25
    Store_413.1514.51 12.86
    Store_510.5011.44 10.25
    Store_616.0417.66 14.19US Stores
    Store_718.6224.37 15.60
    Store_816.0215.69 12.89
     | Show Table
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