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
> Hi Mike,
>
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>>>>> Thanks,
>>>>> Angie
Jay - 26 Sep 2006 07:26 GMT
Mike,
Many thanks for the comprehensive answer, I really appreciate it. I've
been doing some work recently on the seasonal variation in mean used car
prices & this really helps.
I appreciate you taking the time, and am going straight to amazon to
check out your book as I'm in my 6th month in a new job as an analyst &
am sure it would be invaluable.
Many thanks and all the best,
Regards
Jay
> Jay or Jason -
>
[quoted text clipped - 159 lines]
>>>>>> Thanks,
>>>>>> Angie
Jay - 27 Sep 2006 21:05 GMT
Hi Mike,
Sorry to ask more questions but do you mind my asking - in the your last
post you said:
> Yes. Each forecast is for a specific month (Seasonal component) and >
> year
[quoted text clipped - 5 lines]
> 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.
I'm, just having a little trouble understanding. In the file I
downloaded the actual data was all for 2006 (per the dates on sheet1 - 2
or 3 values per month).Is that right as I'm just a little confused as I
thought it was effectively 30 month's consecutive data, hence the chart
on sheet 3. WHich is correct?
Thanks,
Jason I
> Jay or Jason -
>
[quoted text clipped - 159 lines]
>>>>>> Thanks,
>>>>>> Angie
Mike Middleton - 27 Sep 2006 22:00 GMT
Jay -
The actual data are for 30 months. There was a problem with the date format
provided by the original poster shown on my Original Data sheet, so I
converted the dates to integer months 1 to 30 on subsequent sheets. The
projections are for months 31 to 42.
- Mike
> Hi Mike,
>
[quoted text clipped - 186 lines]
>>>>>>> Thanks,
>>>>>>> Angie
Jay - 28 Sep 2006 07:30 GMT
Thanks Mike,. that's what I thought but the dates on the first sheet
kept making me doubt myself:-)
It's much clearer now - *many* thanks for your time & expertise. I
really do appreciate it and I'm sure this knowledge will come in useful.
Kind regards,
Jay
> Jay -
>
[quoted text clipped - 188 lines]
>>>>>>>> Thanks,
>>>>>>>> Angie