Page: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003
Count
1 0.95 7
2 0.91 7
3 1.00 7
4 0.98 7
5 1.01 7
6 1.02 7
7 1.04 7
8 1.05 7
9 1.00 7
10 1.00 7
11 1.00 7
12 1.04 7
Then we can de-seasonalise the data by dividing the factors into the data to create a trend cycle column. Next, we regress the trend cycle against the time and will get a trend column.
Coefficients
Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 3945.164 84.847 46.498 0.000
TIME 84.357 1.519 0.985 55.536 0.000
a Dependent Variable: TRENCYC
From the coefficients table above, the trend equation is:
Trend = 3945.164+84.357 * Time
Then we can create a moving average forecasting column with the factors and trend column. The moving average forecasting equation is:
Forecast = Trend * Factors
Therefore, it can be implied that
Forecast = (3945.164+84.357 * Time) * Factors
Moreover, the errors will be calculated by deducting the forecast data from the actual data in order to see how accurate the forecast is.
Errors = Data – Forecast
See the figures of the result as following table:
Time Data Center Moving Average