Forecasting Air Passenger Data using various models

In this competitive world, transport services have become highly saturated. To operate an existing company or open a new company in the field of transport is very important and need to know the demand forecast. In the field of transport, service providers facing complex challenges due to the competitors. This paper explores the applications of a forecasting model to predict the number of passengers going to travel in the future. It also compares the efficiency of different models like Auto Regressive and Integrated Moving Average (ARIMA), HOLT WINTERS, Naïve, Snaïve, Drift and list their performances.