Analysis of Blue Mesa Inflow Forecast Errors Tom Pagano, 503 414 3010 aka: “Wha’ happa’???”
Forecast Errors
Transcript of Forecast Errors
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Evaluation
The evaluation of Naive Forecasting Techniquesrelies primarily on the comparison of the forecastswith the corresponding actual values
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Evaluation Methods
Mean Error (ME)
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE)
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The ME can be very misleading. A ME value of
zero can mean that the method forecasted the
actual values perfectly (unlikely) or that the
positive and negative errors cancelled eachother out. It tends to Understate the error in
all cases.
n F A ME
n
i
t t /
1
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Evaluation Methods
Mean Error (ME)
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE)
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MAE is a way of dealing with the Understatement
of ME. By using the Absolute values of the error,
the mean gives a better indication of the model’s fit.
MAE A F nt t
i
n
( ) /1
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Evaluation Methods
Mean Error (ME)
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE)
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The MSE eliminates the positive/negativeproblem by squaring the errors. The result
tends to place more emphasis on the larger
errors and, therefore, gives a more conservativemeasure than the MAE.
MSE A F nt t
i
n
2
1
/
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The previous three measures are “seriesspecific;” i.e., they only allow evaluation of
the series that generated the errors.
The next two measures, by using the
percentage of the error relative to the actual,are designed to allow comparison of theresults with different models.
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Evaluation Methods
Mean Error (ME)
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE)
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The MPE is a relative measure of the forecasting
error. It is subject to the “averaging” of the positive
and negative errors.
MPE A F
An
t t
t t
n
1
100 /
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Evaluation Methods
Mean Error (ME)
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Mean Percentage Error (MPE)
Mean Absolute Percentage Error (MAPE)
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MAPE is a comparative measure that does not have
the problem of averaging the positive and negativeerrors. It is relatively easy to use to communicate a
model’s effectiveness.
MAPE A F
A
nt t
t t
n
100
1
/
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Measurement of Forecasting Error Mean Error (ME): The average of all the errors of
forecast for a group of data.
Mean Absolute Error (MAE): The mean, or averageof the absolute values of the errors.
Mean Square Error (MSE): The average of thesquared errors.
Mean Percentage Error (MPE): The average of the percentage errors of a forecast.
Mean Absolute Percentage Error (MAPE): Theaverage of the absolute values of the percentage errorsof a forecast.
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Example:
Nonfarm
Partnership
Tax
Returns:Actual and
Forecast
with = .7
Year Actual Forecast Error
1 1402
2 1458 1402.0 56.0
3 1553 1441.2 111.8
4 1613 1519.5 93.5
5 1676 1584.9 91.1
6 1755 1648.7 106.3
7 1807 1723.1 83.9
8 1824 1781.8 42.2
9 1826 1811.3 14.7
10 1780 1821.6 -41.611 1759 1792.5 -33.5
t t t F A F )1()(1
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Mean Error for the Nonfarm Partnership Forecasted Data
ME ie
number of forecasts
524 3
10
52 43
.
.
Year Actual Forecast Error
1 1402.0
2 1458.0 1402.0 56.0
3 1553.0 1441.2 111.8
4 1613.0 1519.5 93.55 1676.0 1584.9 91.1
6 1755.0 1648.7 106.3
7 1807.0 1723.1 83.9
8 1824.0 1781.8 42.2
9 1826.0 1811.3 14.7
10 1780.0 1821.6 -41.6
11 1759.0 1792.5 -33.5
524.3
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Mean Absolute Error for the Nonfarm Partnership Forecasted Data
MADie
number of forecasts
6745
10
67 45
.
.
Year Actual Forecast Error |Error|
1 1402.0
2 1458.0 1402.0 56.0 56.0
3 1553.0 1441.2 111.8 111.8
4 1613.0 1519.5 93.5 93.55 1676.0 1584.9 91.1 91.1
6 1755.0 1648.7 106.3 106.3
7 1807.0 1723.1 83.9 83.9
8 1824.0 1781.8 42.2 42.2
9 1826.0 1811.3 14.7 14.7
10 1780.0 1821.6 -41.6 41.6
11 1759.0 1792.5 -33.5 33.5
674.5
E
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Mean Square Error for the Nonfarm Partnership Forecasted Data
MSE ie
2
55864 2
10
5586 42
number of forecasts
.
.
Year Actual Forecast Error Error2
1 1402
2 1458 1402.0 56.0 3136.0
3 1553 1441.2 111.8 12499.2
4 1613 1519.5 93.5 8749.75 1676 1584.9 91.1 8292.3
6 1755 1648.7 106.3 11303.6
7 1807 1723.1 83.9 7038.5
8 1824 1781.8 42.2 1778.2
9 1826 1811.3 14.7 214.610 1780 1821.6 -41.6 1731.0
11 1759 1792.5 -33.5 1121.0
55864.2
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Mean Percentage Error for the Nonfarm Partnership Forecasted Data
MPE
i
i
e
X
100
318
10
318%
number of forecasts.
.
Year Actual Forecast Error Error %
1 1402
2 1458 1402.0 56.0 3.8%
3 1553 1441.2 111.8 7.2%
4 1613 1519.5 93.5 5.8%5 1676 1584.9 91.1 5.4%
6 1755 1648.7 106.3 6.1%
7 1807 1723.1 83.9 4.6%
8 1824 1781.8 42.2 2.3%
9 1826 1811.3 14.7 0.8%10 1780 1821.6 -41.6 -2.3%
11 1759 1792.5 -33.5 -1.9%
31.8%
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Mean Absolute Percentage Error for the Nonfarm Partnership Forecasted
Data
MAPE
i
i
e
X
100
403
10
4 03%
number of forecasts
.
.
Year Actual Forecast Error |Error %|
1 1402
2 1458 1402.0 56.0 3.8%
3 1553 1441.2 111.8 7.2%
4 1613 1519.5 93.5 5.8%
5 1676 1584.9 91.1 5.4%
6 1755 1648.7 106.3 6.1%
7 1807 1723.1 83.9 4.6%
8 1824 1781.8 42.2 2.3%
9 1826 1811.3 14.7 0.8%
10 1780 1821.6 -41.6 2.3%
11 1759 1792.5 -33.5 1.9%
40.3%
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Use of Error MeasuresTo identify the best forecasting method
• Use error measure to identify the best value
for the parameters of a specific method.• Use error measure to identify the best
method.
• Use MSE and MAE for both of thesesituations. Note that MSE tends toemphasize large errors.
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Use of Error Measures, continued
Forecast bias is the tendency of a
forecasting method to over or under predict.
The mean error, ME, measures the forecast
bias.
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