Sales Forecasting by Using Time Series Decomposition [Minitab]

O

ozkaynakm

#1
Hi,
Nowadays, I try to forecast the sales figures of 8 weeks to come by using performed sales. I have a lot of [weekly] data belong to previous years starting from 2005 up to now.
==> How many weeks should be taken into account for Decomposition?
==> What about your comments If I prefer using the other methods [like Winters, double exp...]. Please note that the minimum accuracy measures are obtained from decomposition. [Also the seasonality and trend is in question regarding my data].
I enter the seasonal length as 52 since a period contains 52 weeks.
Finally, I've obtained accuracy measures;
MAPE 43
MAD 18051
MSD 682263979
Especially, I am not comfortable because of high MSD value. It appears as a very big value to me.
==> Is it reliable to use the forecast numbers extracted from this analysis in view of these measures?
Thanks in advance.
Okan
 
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Stijloor

Staff member
Super Moderator
#2
Hi,
Nowadays, I try to forecast the sales figures of 8 weeks to come by using performed sales. I have a lot of [weekly] data belong to previous years starting from 2005 up to now.
==> How many weeks should be taken into account for Decomposition?
==> What about your comments If I prefer using the other methods [like Winters, double exp...]. Please note that the minimum accuracy measures are obtained from decomposition. [Also the seasonality and trend is in question regarding my data].
I enter the seasonal length as 52 since a period contains 52 weeks.
Finally, I've obtained accuracy measures;
MAPE 43
MAD 18051
MSD 682263979
Especially, I am not comfortable because of high MSD value. It appears as a very big value to me.
==> Is it reliable to use the forecast numbers extracted from this analysis in view of these measures?
Thanks in advance.
Okan
Any Minitab experts who can help Okan?

Thank you!

Stijloor.
 

Tim Folkerts

Trusted Information Resource
#3
From Minitab Help:


MAPE
, or Mean Absolute Percentage Error, measures the accuracy of fitted time series values. It expresses accuracy as a percentage.

MAD, which stands for Mean Absolute Deviation, measures the accuracy of fitted time series values. It expresses accuracy in the same units as the data, which helps conceptualize the amount of error.

MSD stands for Mean Squared Deviation. MSD is always computed using the same denominator, n, regardless of the model, so you can compare MSD values across models. MSD is a more sensitive measure of an unusually large forecast error than MAD.



So since MAPE = 43, the model is on average 43% off from the data. That seems like a fairly large number so future predictions would alos likely be off by at least that percentage.

MAD is very similar, but it is not expressed as a percent. If X is your average sales, then (approximately) 43% of X = MAD. So I am predicting that your raw numbers average somewhere around 42,000 and deviate about 18,000 from that value.

MSD squares the deviation, so if you want to compare it to MAD, you should take the square root. (682263979)^0.5 = 26,120. This is noticeably bigger than MAD = 18,051, suggesting a few points with "unusually large forecast error", according to the Miniitab help.


Tim


 
O

ozkaynakm

#4
Thank you Tim.

The variable that I used for Decomposition is weekly sales data. It is not average. The sum of sales performed during 1 week constitutes one row.

As far as I understand that the forecast results of this analysis is not reliable.

When I've taken a glance at the residual plots, the residuals show nonnormality. This is the another handicape when we consider the analysis.

These sales numbers have very big standart deviation according to the weeks. It could be one of the reason of this bad result.
 
B

Bill Pflanz

#5
It is incorrect to compare MAPE, MAD and MSD since they are just different methods of calculating forecast error. The correct way to evaluate a forecast is to use a number of forecast methods, calculate the forecast error using one (and only one) forecasting error method and choose the forecast method with the lowest error. Common forecasting methods are moving average, weighted moving average, exponential smoothing and seasonal variations.

The number of weeks is not that important unless there are cyclical or seasonal trends. If your business is seasonal then you need to use a seasonal forecast method with decompostion. I would recommend using 3 years of quarterly data.

Bill Pflanz
 
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