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Marketing analysis and forcasting
Finance & Accounting
Pages 6 (1506 words)
Hughes Travel PLC Forecasts for the year 2011 Type your name here 4/29/2011 Table of Contents Table of Contents 2 Analysis of the Time Series Data and its Structure 3 Forecasting Methods 4 Forecasts using the ARIMA Model 7 Error Measures 8 References 10 Appendix 11 Time-Series Analysis 11 Exponential Smoothing 16 ARIMA Model 19 Analysis of the Time Series Data and its Structure A time series is the combination of observations recorded sequentially over time.
The data available is of Hughes Travel PLC monthly travel data collected over the span of January 1986 – December 2010. It consists of two variables, namely: number of overseas visitors travelling to the UK and the number of UK residents travelling abroad. The time interval of data collection for both the variables is a month. As both the variable data is independent of each other, hence, we have two univariate time series. The data does not depict a particular trend. Analysis of UK Residents Time Series Figure 1 of appendix A shows the month wise distribution of UK residents travelling abroad. The graph shows that highest number of UK residents travel abroad during the months of August, September, and July. The graph shows that August has had highest number of UK residents travel abroad and it has happened consistently for the past 25 years. Similarly, figure 2 of appendix A shows the cumulative data on UK residents travelling abroad on a yearly basis. The data shows a steady rise in the number of UK residents travelling abroad with the highest being year 2010. Figure 1 in appendix A also depicts that UK residents travel least during the months of December, January, February. ...
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