Airline Industry has become one of the customer choice as their transport. After a few tragedy happen such as the September 11 terrorist attacks, economic slowdown, several accidents involving airplanes and many mores, this has impacted passenger trust towards airline industry (Radoslaw, 2014). Due to this, there are numerous of study regarding this issue. For example, survey are conducted in order to have an overview a passengers’ needs towards airline industry (Asrah 2012). In this research, the survey called People NEWS (Needs, Expectation, Wants and Satisfaction) was conducted to obtain public opinion regarding air travel safety and process in Malaysia. In this paper, they concentrate more on check in and check out steps. Eventually, this survey can be a useful recommendations not only for airlines’ company but also to government for future development of airlines. In a research conducting in Nigeria, they suggest the need for the government to improve the airline system (Aderamo, 2010). For determine future planning, they collect the data on passenger, aircraft and cargo movement to determine the pattern of airlines industry in order to have a future planning.
Besides that, it is also important to improve our understanding towards airlines’ passenger decision making (J. W. Park, Robertson, and Wu ,2004). Service with high quality has become a requirement for gaining customer support and increase the profit. Giving a high quality service has become one of the marketing needs (Ostrowski et al., 1993). It is vital to understand what the passenger need and expect from the service organizations (Jin and Julie, 2000). The effect of airline passengers’ expectations on service perception and passenger satisfaction has to be fully investigated. In Malaysia, (O’Connell and Williams ,2005) seeks passengers’ perceptions of low cost airlines and full service carriers. Survey have been conducted to determine why passenger are choosing one particular airline over another. The growth of low cost airline industry has become one of the passengers’ choice recently (O’Connell and Williams, 2005). Graham (2006) identifies the majority of the low-cost airline demand is from leisure travellers while Mason (2005) identifies the number of business traveller that used low cost carrier are increasing and they also view the low cost carrier as good indicator towards business demand. Thus, to maintain consumer interest, airlines need to continue to innovate, providing tourism destinations which meet these requirement.
2.2 Forecasting method
In order to use forecasting techniques, different situation used different kind of techniques. Firstly, we have to know different forecasting techniques if we want to do a forecasting according to its situation. Some of the techniques are moving average, exponential smoothing (simple, Holt’s method, and winters method), linear regression and Box–Jenkins models. Forecasting have many fields including business and industry, government, economics, environmental sciences, medicine, social science, politics, and finance (Anderson 1979). Forecasting are often classified as short-term, medium-term, and long-term (O’Connell and Williams ,2005). For example, in the research by (D. C. Park et al. 1991), this paper presents an artificial neural network(ANN) approach to electric load forecasting. Neural networks (NNs) have been vigorously promoted in the computer science literature for tackling a wide variety of problems. Recently, statisticians have started to investigate whether NNs are useful for tackling various statistical problems (Cheng and Titterington, 1994) and there has been particular attention to pattern recognition (Bishop, 1995; Ripley, 1996). NNs also appear to have potential application in time series modelling and forecasting but nearly all such work has been published outside the mainstream statistical literature. Besides that, they are also different forecasting techniques that can be used that suited with the field needed. One of the forecasting method was geometric Brownian motion (GBM). This method has a criteria of stationary, normally distributed and independent (Marathe and Ryan 2005). In paper by (Asrah, 2013), they has been used GBM method to forecast the number of Air Asia passenger.
2.3 Forecasting Airline Passenger by using Box Jenkin Method
Forecasting airline has been known around the world. For example the research by (Hong et al. 2015), they forecast airport passenger traffic for Hong Kong International Airport and also predict its future growth trend to 2015. They believe that forecasting result can give an overview regarding the developing of HKIA’s future passenger traffic. Thus, in this research they used Box Jenkin ARIMA model for forecasting HKIA’s future passenger throughput. According to prior study, it stated that ARIMA model can accurately forecast airport traffic demands (Abdelghany and Guzhva, 2010). SARIMA model was used to model HKIA’s passenger traffic. SARIMA models predict a steady growth in future airport passenger traf?c, Hong Kong. In addition, scenario analysis suggests that Hong Kong airport’s future passenger traf?c will continue to grow in different magnitudes. In Ming et al (2014), they also applied ARIMA models to forecast air traffic passengers travel. Thus, ARIMA model are suitable to be used as a model to forecast by using airline data.
In a past decade, technological development and the global economic crisis are types of development that can affect the airline industry. The research by (Rdoslaw and Almas 2010) was conducted to investigate time series analysis of airline industry. They decided to use ARIMA model as it suitable with airline market because it allows us to process regular seasonal fluctuation time series (McLeod, Hipel, and Lennox 1977). They use ARIMA (0,1,1)×(0,1,1)12 as the final model for measuring the prediction performance of the model. The purpose of this research basically to analyse the exact future growth of passenger transportation. Thus, in the future it can be expected that an anticipated average of 10,000 more passengers will utilize the world airlines services every month. Furthermore, the research by Asrah et al (2018) they forecast the number of passenger Malaysian Airline (MAS) by using Box Jenkins method. The suitable time series model for data in 2009 is SARIMA (0,0,1)(1,0,0) while the suitable time series model for data in 2012 is SARIMA (2,0,0)(0,1,1). In this study, we use Box Jenkins method to forecast the number of passenger airline of Air Asia.