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Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003
Jul 103 14723 13984 12929 14555.76 167.24
Aug 104 13215 14085 12325 14206.89 -991.89
Sep 105 12989 12112 11358 12711.23 277.77
Oct 106 13494 14723 13247 15087.19 -1593.19
Nov 107 12942 13215 12849 14166.08 -1224.08
Dec 108 13922 12989 12635 13927.56 -5.56
2003 Jan 109 13598 13494 12762 14231.93 -633.93
Feb 110 12272 12942 11965 13470.75 -1198.75
Sum of squared errors = 10712844.2
Mean-squared error (MSE) = 765203.16
Root-mean-squared error (RMSE) = 874.76
This RMSE is about 6.63 % of the mean for the rest of data during the forecast period
From the table above, we can notice the errors as illustrated the graph below which is plotted from rest data against forecast data of 2002-2003.
However, there are some errors at the end, overall the plot graph below (During 1994-2003) shows that the predict data relatively fit the real data.
From the graph above, it illustrated the actual data (red line), Decomposition forecasting (blue line), and Autocorrelation forecasting (green line). Some periods Decomposition model is closer and fitter than Autocorrelation model, but some periods do contrast. Therefore, it is relatively difficult to say which model is better because both models are quite close to the real data.
However, it can be seen the error of these two models as following graph:
Decomposition Model:
Forecast = (3945.164 + 84.357 * Time) * Factors
Autocorrelation Model:
Forecast = 0.439 * (data lagged3) + 0.651 * (data lagged12)
From these equations, it demonstrates that decomposition model depends on the trend running following time and some [next page]