TIME SERIES FORECASTING VIA GENETIC ALGORITHM FOR TURKISH AIR TRANSPORT MARKET
Due to strong traffic momentum since the sector deregulation in Turkey passenger numbers has risen at an approximately CAGR (Compound Annual Growth Rate) of 16% between 2003 and 2013, which means three times the real GDP (Gross Domestic Product) growth in the same period. We believe that Turkey’s geographically advantageous position, increasing attractiveness as a tourism destination, and the government’s supportive approach to the sector are the secular reasons for this average growth. A long-term forecast of the air transportation is crucial regarding the precautions that will be taken in the future. It is also an essential input for an investment planning. In the field of air transportation forecasting, there are a number of empirical models, which can be classified as judgemental, causal and time series. Time series forecasting is an important area of forecasting in which past observations of the same variables are collected and analysed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. In this study, forecasting models for time series are reviewed. Aircraft, passenger, and cargo statistics between 2007-2015 years in Turkey are gathered and used to construct forecasting models for each quantity. Based on the mathematical models constructed by using genetic algorithm from heuristics, we project a 24% increase in Turkish aircraft traffic, a 50% increase in Turkish air passenger traffic, and a 34% increase in Turkish air cargo traffic between 2016-2020 years.
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