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Forecasting models and errors

Former Member
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Hi Expeing

I have following queries with regard to the forecasting Techniques:

1)How can we identify the periods in Seasonality?How to judge the seasonal periods to be taken for running the seasonal forecasting models?

2)How the errors are used in judging the forecast?As we have 6 errors what is the importance of the errors(MAD,MAPE,MSC,MPE,RMSC)?

What error should be taken as consideration while forecasting?

Why some planners only consider MAD and some planners only consider MAPE ,MSC while judging the forecast models?What is the difference between MAD,MAPE,MSC,MPE &RMSE

Please throw some light on the above questions.Your reply is highly awarded.

with regards

sai

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Answers (2)

Answers (2)

Former Member
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Hi,

1) I don't have experience in seasonal planning....but..I think this is a business decision how many periods in a season.

2) "How the errors are used in judging the forecast?As we have 6 errors what is the importance of the errors(MAD,MAPE,MSC,MPE,RMSC)?

What error should be taken as consideration while forecasting?

Why some planners only consider MAD and some planners only consider MAPE ,MSC while judging the forecast models?What is the difference between MAD,MAPE,MSC,MPE &RMSE"

I hope you remember your [post|] on forecast errors.

Some planners prefer MAD as it is average of errors and few prefer MAPE as it is mean of absolute...The users I have worked with usually take a forecast that they believe that they know for sure is a good forecast (this is usually done outside APO) and run the forecast in APO comparing the errors. They see which error works best for them.

If they are not sure I just suggest using MAD.

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Dear Sai,

No answer can be exhaustive .I have tried to explain as short as possible.

For Identification of periods of seasonality ,it is important to clean the historical data from abnormal high and low sales.Abnormal high sales can be caused by sporadic opportunities and low sales may happen due to natural calamities. These unnatural sales should be removed in consultation with business people to get clear picture.

Seasonal variation is measured in terms of an index, called seasonal index. It is an average that indicates the percentage of an actual observation relative to what it would be if no seasonal variation in a particular period is present

Measuring Forecast Errors

Method and Purpose

Mean squared error (MSE): Measures the dispersion of forecast errors; large errors get more weight than when using MAD. Therefore, it is sensitive to non-normal data contaminated by outliers, and such data are common.

Mean absolute deviation(MAD):Measures the dispersion of forecast errors.Measures the size of errors in units. Though it is a good measure for single product forecast, but if we aggregate MAD over multiple items, there is a possibility of high volume products dominating the results. MAD is a linear metric for error and gives same weight to all errors , large or small.

Mean absolute percent error (MAPE):Measures the dispersion of forecast errors relative to the level of demand.It measures the size of error in percentage terms. A MAPE of .19 suggests that on average the difference between the forecasted and actual values is 19%. MAPE is scale sensitive and therefore meaningless for low volume data or data with zero demand periods.

RMSE:The quadratic error provides estimates that are more linked to variance and standard deviation of demand distribution. RMSE is a quadratic metric for error and tends to overweight large errors.

Regards,

Samir Baruah