Timeline for Detecting initial trend or outliers
Current License: CC BY-SA 3.0
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| Oct 15, 2012 at 17:37 | comment | added | IrishStat | @whuber The detection of a level shift is accomplishe by Intervention Detection (note this is not the same as Interevntion Modeling) Essentially it is sequence of trial baloons taht are evaluated based upon the effeciveness of the candidate structure. The best way to understand this is to see Tsay unc.edu/~jbhill/tsay.pdf. | |
| Oct 15, 2012 at 16:18 | comment | added | IrishStat | @whuber With 10 values as above , one would want to apply the least disruptive remedy. Theree is no doubtthat one could argue that there has been an increase in variance at period 8. The consequences of such a hypothesis going forward could have downsize effects. Untreated pulses I.E. changes in the mean of the errors can be mis-diagnosed as changes in the level, changes in parameters, changes in variance or more simply and less drastic ...just simply unusual i.e. pulses. As more observations become available it will become clearer as to the most probable cause of the exceptional activity. | |
| Oct 1, 2012 at 20:02 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Oct 1, 2012 at 19:04 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Oct 1, 2012 at 17:18 | comment | added | whuber♦ | Could you be a little more specific about how you will (a) detect level shifts in such short sequences, (b) exploit the presumption that this is not a level shift but rather a change in variance, (c) exploit the presumption that the variance will initially decrease and then level off, and (d) capitalize on having data from many parallel test procedures. (I haven't seen any examples so far of Autobox or SAS handling any of these special characteristics of the problem, and all of them provide powerful opportunities for better procedures than usually found in time series software.) | |
| Oct 1, 2012 at 17:08 | history | answered | IrishStat | CC BY-SA 3.0 |