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STLESDTEST


outlier gts
Since v1.0.0
Available on all platforms
See also

The 'STLESDTEST' function detects outliers in a Geo Time Series™ (or a LIST of Geo Time Series™) which has a seasonal part.

The seasonal part and the trend part of the Geo Time Series™ are extracted using STL decomposition, then an ESDTEST is performed on the remainder.

This function only applies to bucketized GTS of type DOUBLE.

References

Cleveland, Robert B., et al. "STL: A seasonal-trend decomposition procedure based on loess." Journal of Official Statistics 6.1 (1990): 3-73. Rosner, Bernard (May 1983), "Percentage Points for a Generalized ESD Many-Outlier Procedure",Technometrics, 25(2), pp. 165-172.

Signature

Examples

// Macro used to generate an approximately normal distribution (using central limit theorem) <% RAND RAND RAND RAND RAND RAND + + + + + 3.0 - %> 'normal' STORE // we generate a GTS with an approximately normal distribution [ NEWGTS 1 50 <% NaN NaN NaN @normal ADDVALUE %> FOR // we add outliers (> 3.0 in absolute value) // Note that we do this before adding seasonal and trend components 25 NaN NaN NaN -3.9 ADDVALUE 36 NaN NaN NaN 3.8 ADDVALUE DEDUP // we generate a periodic GTS of mean 0 NEWGTS 1 50 <% NaN NaN NaN 4 PICK 10 % 4.5 - ADDVALUE %> FOR // we generate a trend GTS (linear raise y=x) NEWGTS 1 50 <% NaN NaN NaN 4 PICK ADDVALUE %> FOR ] // we sum up the 3 components: remainder, seasonal and trend [ SWAP [] reducer.sum ] REDUCE 'sum' RENAME // bucketize [ SWAP bucketizer.first 0 1 50 ] BUCKETIZE 0 GET // we call STLESDTEST 10 2 STLESDTEST