rts and analysed separately. After, the components are rebuilt and the forecast is made.(Table 5)The regression models (linear and exponential) use the built in regression of Excel to forecast the values. The different types are needed because the values of data may be fit to a straight line or to an exponential curve. To be able to get the possible best results I must conduct them all.(Table 6,7)Having conducted the different procedures it is time to compare them to each other.MSEMADMAPENaive forecast 1 period ahead74644.33254.5617.45%Moving Average 3quarter37631.92168.7411.51%Moving Average 4quarter24499.71136.209.23%Exponential Smoothing a=0.229485.20146.259.98%Exponential Smoothing a=0.433731.96160.6210.97%Exponential Smoothing a=0.854578.95213.1314.59%Deseasonalisation7010.5062.044.43%Double Moving Average 3quarter71216.02232.2015.89%Linear Regression23354.74134.489.33%Exponential Regression23343.68132.579.12%From the table it is clearly seen that the values of error terms are the smallest for the deseasonalisation model in all of the three measures of accuracy. In Graph 2 it is seen that the forecasted values closely fit to the past data. This indicates that I have to forecast with this method to be the most effective.The deseasonalization model operates with splitting the time series into components, which are the trend, the cyclical, the seasonal and the irregular component.In time series the trend component is the long-term component that represents the growth or decline in the series over a period of time. In the case of the Consolidated Edison Company, this trend effect is a continuous growth, which has started since 1985. This trend effect can be related to the changes in the economy- inflation and the continuously growing consumption.The cyclical component is the wavelike fluctuation around the trend. Any regular pattern above or below the trend line might be related to the effect of cyclical component. In this case this co...
