Jay or Jason -
That workbook illustrates a method for "Classical Time Series
Decomposition," a standard method for analyzing seasonality. You may be able
to get more information using a google search. (It's one of three methods
described in Chapter 20, Time Series Seasonality, in my book, Data Analysis
Using Microsoft Excel: Updated for Office XP.)
What exactly does the ratio signify? - The relationship between the actual
(seasonal) data and the trend? <
Yes. This method uses the multiplicative model, i.e., actual values are
described with three multiplicative components: Actual = Trend * Seasonal *
Random (unexplained).
The ratio of Actual to Trend yields a result with only the (Seasonal *
Random) components.
And am I right in thinking the 'Average ratio' is the average of this
relationship for each month i.e All Jan, Feb ratios average etc? <
Yes. All Jan ratios are averaged, all Feb ratios are averaged, etc.
If a visual check of the ratios shows wide variation, it might be better to
use a trimmed mean (TRIMMEAN function) or median (MEDIAN function).
The result of averaging the (Seasonal * Random) ratios yields an average for
the Seasonal component of the model.
The part I'm a little hazy about is the last sheet, where the Trend is
multiplied by the Average to give the 'Forecast'. I'm a little confused as
to how this is a Forecast? <
Excel's TREND function projects the long-run behavior into the future (a
"forecast"), and the Average of Ratios adjusts that long-run behavior to
include the typical seasonal variation (a better forecast).
Since we are using a multiplicative model, we multiply the Trend forecast
times the Seasonal forecast to get a forecast of Actual that contains both
components.
Is it a forecast for a set time period into the future? <
Yes. Each forecast is for a specific month (Seasonal component) and year
(projection of Trend component).
If so, how far a forecast. <
That depends on your judgment about how reasonable it is to make such
projections (both the linear trend component and the average seasonal
variation component). The original poster wanted a 12-month forecast, which
seems reasonable from visual inspection of the time series data.
- Mike
http://www.mikemiddleton.com