This operator learns deviation of (each point of a) sequence. The sequence needs a counter so that the operator can distinguish multiple sequences and the values within a sequence. The operator uses the online-learn algorithm of the DeviationLearn operator.

### Example

The operator gets the following input:

CounterValue
08
15
220
04
16
222

This were two sequences, because a new sequence starts if the counter of the next tuple is smaller than the previous counter. Every sequence had three tuples. The output on port 1 would be the following:

groupmeanstandardDeviation
08.00.0
15.00.0
220.00.0
06.02.828
15.50.707
221.01.414

### Parameters

• attribute Name of the attribute which should be analysed
• sequencesToLearn The number of (correct) sequencesto learn from. The first x sequences will define the perfect sequence the others are compared to. If set to 0, the operator will not stop to learn (learn infinity sequences). Default is 0.
• GROUP_BY To group the tuples into the single parts of the sequence.
• fastGrouping Use hash code instead of tuple compare to create group. Potentially unsafe!

### Example

The example PQL code shows, how to use the operator. The GROUP_BY parameter is very important because it is used to distinguish the single values within one sequence.

 ```#PARSER PQL #RUNQUERY /// Values above 50 will be 'true' (which means that the current sequence starts / runs) and smaller values to 'false' (means: sequence ended) stateInfo = MAP({ expressions = ['temp', ['temp > 50', 'state']] }, System.manual ) /// The elements within one sequence will be counted (starts from 1 with each new sequence) sequence = MAP({ expressions = ['temp','counter(state)'] }, stateInfo ) /// The tuple which marks the end of the sequence (and itself is not part of the sequence) has the counter_state_ 0 and will be filtered out onlySequence = SELECT({PREDICATE = 'counter_state_ > 0'}, sequence) /// Learn how a "normal" sequence is. The first 15 sequences will be learned and used as the definition of "normal" sequenceLearn = DEVIATIONSEQUENCELEARN({ group_by = ['counter_state_'], attribute = 'temp', sequencesToLearn = 15 }, onlySequence ) /// Check, if the current tuple of this sequence differes from the normal tuples of the sequence at the specific point of the sequence sequenceAnalysis = DEVIATIONSEQUENCEANOMALYDETECTION({ interval = 4.0, standardDeviationLearnAttribute = 'standardDeviation', group_by = ['group'], meanLearnAttribute = 'mean', valueDataAttribute = 'temp' }, 0:sequenceLearn, 1:sequenceLearn )```