Clear Skies Airlines Case Study

After conducting time series data, I have found out that this data is seasonal because the graph of this data shows there is an upward increase every July in each year, the time series exhibits regular fluctuations during every July in each year. For instance, this equation (y = 2.3347x + 100.47) explains that there is a coefficient of trend that’s moving upwards Moreover, every July the demand of Clear Skies Airlines (CSA) is very good
Therefore, the number of passengers that travel overseas from Pleasantville using CSA’s services, shows a strong increasing trend, with strong seasonality. On the basis of the cyclical travel patterns, we can clearly see that the data exhibits a seasonal behavior. But we can also see an upward trend, suggesting
So the reason I have chosen MAD in exponential smoothing is that, in order to determine which of the values of alpha that are being considered as the lowest value, MAD can be used to identify which alpha is more effective and useful during forecasting.
Mean square error help in order to determine the minimized optimal value of the constant, so in this method Clear Skies Airlines (CSA) can compute the optimal value of the exponential smoothing constant and also to improve the accuracy of the forecasting.
According to the result I got from data series, I have chosen multiplicative model to forecast the number of passengers that travel overseas from Pleasantville using CSA’s services in the year 2008. The reason for the chosen method is that by looking at the graph, the data keep on increasing year by year, the size of the seasonal fluctuations vary depending overall level of the timer series. Generally, the decomposition method provides an easy way to calculate seasonally adjusted data, when it comes to multiplicative model the seasonally adjusted data are calculated by dividing the actual observation by the seasonal