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Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003
9460 10597 10450
2000 9530 10026 10833 9343 11156 10806 10773 10882 9906 10857 10892 10709
2001 11143 10023 11062 11227 11898 11791 12929 12325 11358 13247 12849 12635
2002 12762 11965 12679 13984 14085 12112 14723 13215 12989 13494 12942 13922
2003 109 110
Resource: http://www.statistics.gov.uk/statbase/TSDtimezone.asp (Accessed 23/04/03)
The figure shows Banks consumer credit: Gross lending on a monthly basis starting with the first month of 1994 and ending with the twelfth month of 2002. And we will use the data in 2002 to check on the forecast.
As seen from the graph above, the time series data moves upward over a period of time so it can be said that the data shows a fairly strongly positive trend, so that we might expect high autocorrelation coefficients. Considering ACF, it supports this expectation because it is significantly different from zero. Autocorrelation at lag 1 is 0.945 and move downwards to 0.581 at lag 16.
Afterwards, we will choose data from 1994-2001 to model.
2. Try a classical decomposition method on part of the data and check it against the rest of the data.
The ACF of the actual data shows that there is a trend or cycle or both, so we should look at the ACF of the first difference.
From the ACF of the first difference, we could use the 12 points moving average to smooth the data. Subsequently, we will calculate the twelve point moving average, seasonal estimate, trend-cycle estimate and forecast based on the trend-cycle and the seasonal ratios.
After calculating center moving average and getting seasonal estimate (ratio), we will find out the factors as following table:
Sequence Mean [next page]