Could you please explain how to work with Outlier Correction

could you please explain how to work with Outlier Correction

Hi sr,
Actually an outlier correction is an historical value that lies the tolerance lane.
the apo sys calculate this tolerance lane on the basis of the sigma factor.
It then correct any historical value that lies outside the upper or lower band of this tolerance lane.
So, that it corresponds to the calculated ex- post value for that point in time.
this option is available in Master forecast profile /sapapo/mc96b in Univariate profile( control parameters) tab.
If you have set the outlier correction indicator, then the sys first calculate an ex-post forecast with the selected forecasting techniques.
In the next step, the sys calculates a tolerance threshold T.
where T is calculated as T = sigma * MAD (Mean Absolute Deviation)
Here the Sigma factor defines the width of the tolerance rangefor automatic outlier correction.
it defines the permissible number of standard deviations.
A small sigma factor means a low tolerance, and a large number of outliers that are detected and corrected.
The default Sigma factor is 1.25.
If you set the Sigma factor, SAP recommends that you set it betw 0.6 and 2.
I hope this is helpful for you.
Regards,
Pullaiah

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    test@ora> -- Query 1 - using the non-equi (theta) join
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          1000 able           900 baker
          1000 able           900 charles
          1000 able           800 delta
          1000 able           700 eddy
          1000 able           700 fred
          1000 able           700 george
          1000 able           700 george
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           900 baker          900 charles
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           900 baker          700 george
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           900 charles        900 charles
           900 charles        800 delta
           900 charles        700 eddy
           900 charles        700 fred
           900 charles        700 george
           900 charles        700 george
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           800 delta          700 eddy
           800 delta          700 fred
           800 delta          700 george
           800 delta          700 george
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           700 eddy           700 fred
           700 eddy           700 george
           700 eddy           700 george
           700 fred           700 eddy
           700 fred           700 fred
           700 fred           700 george
           700 fred           700 george
           700 george         700 eddy
           700 george         700 fred
           700 george         700 george
           700 george         700 george
           700 george         700 eddy
           700 george         700 fred
           700 george         700 george
           700 george         700 george
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    test@ora> --
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      3    select 'baker',   900 from dual union all
      4    select 'charles', 900 from dual union all
      5    select 'delta',   800 from dual union all
      6    select 'eddy',    700 from dual union all
      7    select 'fred',    700 from dual union all
      8    select 'george',  700 from dual union all
      9    select 'george',  700 from dual)
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    13         COUNT(p1.sal) as cnt_p1_sal,
    14         COUNT(DISTINCT p1.sal) as cnt_dist_p1_sal,
    15         MIN(p1.sal) as min_p1_sal,
    16         MAX(p1.sal) as max_p1_sal
    17    FROM psal p1, psal p2
    18   WHERE p1.sal >= p2.sal
    19  GROUP BY p2.sal;
        P2_SAL        CNT CNT_P1_SAL CNT_DIST_P1_SAL MIN_P1_SAL MAX_P1_SAL
           700         32         32               4        700       1000
           800          4          4               3        800       1000
           900          6          6               2        900       1000
          1000          1          1               1       1000       1000
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    test@ora>Now, if you group by p2.sal in the output of query 1, and check the number of distinct p1.sal, min of p1.sal etc. you see that for p2.sal values - 800, 900 and 1000, there are 3 or less p1.sal values associated.
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    test@ora>
    test@ora> --
    test@ora> -- Query 3 - GROUP BY and HAVING
    test@ora> --
    test@ora> with psal as (
      2    select 'able' as name, 1000 as sal from dual union all
      3    select 'baker',   900 from dual union all
      4    select 'charles', 900 from dual union all
      5    select 'delta',   800 from dual union all
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      7    select 'fred',    700 from dual union all
      8    select 'george',  700 from dual union all
      9    select 'george',  700 from dual)
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    13         COUNT(p1.sal) as cnt_p1_sal,
    14         COUNT(DISTINCT p1.sal) as cnt_dist_p1_sal,
    15         MIN(p1.sal) as min_p1_sal,
    16         MAX(p1.sal) as max_p1_sal
    17    FROM psal p1, psal p2
    18   WHERE p1.sal >= p2.sal
    19  GROUP BY p2.sal
    20  HAVING COUNT(DISTINCT p1.sal) <= 3;
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           900          6          6               2        900       1000
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    test@ora>
    test@ora>
    test@ora>That's what you are doing in that query.
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    test@ora>
    test@ora> --
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      7    select 'fred',    700 from dual union all
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    test@ora>
    test@ora>And for Top-3 distinct salaries:
    test@ora>
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           800
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    test@ora>
    test@ora>You may also want to check out the RANK and DENSE_RANK analytic functions.
    RANK:
    http://download.oracle.com/docs/cd/B19306_01/server.102/b14200/functions123.htm#SQLRF00690
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    http://download.oracle.com/docs/cd/B19306_01/server.102/b14200/functions043.htm#SQLRF00633
    HTH
    isotope

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