Decomposition
Forecasting can prove to be useful in making financial decisions for a future period. However,
firms should be as accurate as possible in their forecasting in order to avoid excess costs or a
decrease in sales.
There are two main approaches to forecasting- explanatory and extrapolation methods. Since,
the explanatory approach is more difficult to implement; the extrapolation (time series) approach
is used here.
The data from the time series should be visualized in a graph to determine if any patterns exist in
the data at certain times. If there are any trends in the data, then we should decompose the sales
data. In other words, we are removing the trends or data that is influencing sales.
There are four types of sales components for time series analysis: 1) seasonalities; 2) growth
trends, 3) cyclic oscillations 4) irregularities. We should identify which of the sales components
identified above are in the sales data. Afterwards, we must remove the component effects from
the sales data (decomposition). Then, we should make a sales forecast by putting the
components back in the new sales estimates (after the effects are measured).
Next, the original sales data should be re-visited to remove any sales that are a result of a certain
season (i.e. Christmas). We must remove the seasonal effects in the data, since these increases in
sales will not continue in the next period. One way that this can be accomplished is through a
ratio to moving average approach. Then we measure the seasonal effects by calculating a series
of indexes. After computing the indexes, we measure how far above or below a given period
stands in comparison to the usual business’ sales period. To calculate the seasonal indexes:
1) Compute the average.
2) Compute the ratio of actual sales in each period to the average.
3) Average the above ratios for all periods for all years.
4) Correct the averaged ratios for any error to get the seasonal index set.
5) Divide the original sales by the seasonal index to get the deseasonalized sales levels.
Using the deseasonalized data, we now consider the growth trend. This is done by fitting a line
to the sales data for all given years.
Next we measure the cyclical effects by calculating a series of cyclic indexes. First, we
determine if the period’s data is above or below the cyclic average for sales. A moving average
approach is useful here as well to smooth out the cyclic plot.
Finally, a sales forecast can be made. This is done by computing the future sales trend level
using the trend line equation, multiply the sales trend level from the previous step by the period
seasonal index to include seasonal effects. Then, multiply the results by the projected cyclic
index to include cyclic effects and get the final forecast result.