Passenger Flow Forecast of Catering Business based on Autoregressive Integrated Moving Average and Smoothing Index Prediction Model

Abstract

As people’s living standards improve, more and more catering enterprises are trying to develop more reasonable business development by using machine learning methods for data mining. Through the prediction for restaurant patronage, the dining experience of customers can be improved, and at the same time, the restaurant industry can achieve more efficient operation. This paper proposes the use of ARIMA time series model and smoothed exponential prediction model algorithm for restaurant patronage prediction, and demonstrates the rationality and feasibility of the idea through experiments. It provides new ideas for applying machine learning techniques and achieving efficient operation in the restaurant industry.