ioned, all of these measures test the errors, and when the values of measures are the smallest in a method, that method seems to be the most accurate one.Now, I will conduct the different methods one by one.The first technique is the naive approach. The essence of this approach is that it uses the value of the current period as the forecast for the next period. This model is rarely the best one because it does not take the seasonality and the economic changes into consideration.(Table I)The next method I have conducted is the moving averages. This technique uses several past time periods as the forecast for the next period. I averaged three and four quarters to get the possible best one, but it has turned up that the three quarter one has overestimated, while the four quarter one has underestimated the values a bit. From the graph we can see that the four quarter moving average method does not take the seasonality into consideration, therefor it calculates only average values.(Table 2)After the moving averages procedure I conducted the exponential smoothing method, which uses a weighted average of past time series values to get a smoothed forecast. This model decreases the effects of past data and this way creates more accurate forecasts for the future. I used three different weights; the value of 0.2 and the 0.4 and the 0.8. Among them the model weighted by 0.2 was the most accurate one.(Table 3)The double moving average model is an improved variation of the moving averages models. Although a better result was hoped from this technique I must say that the result was worse than the previous ones’. It is seen in the graph that this method is continually overestimating. It can be related to the wrong model building. It would be interesting to test the four quarter one as well.(Table 4)It is said that usually the best procedure is the deseasonalisation technique, because this method splits the components of the time series up into pa...
