This documentation is for WSO2 Complex Event Processor 4.0.0. View documentation for the latest release.
WSO2 Complex Event Processor is succeeded by WSO2 Stream Processor. To view the latest documentation for WSO2 SP, see WSO2 Stream Processor Documentation.
||
Skip to end of metadata
Go to start of metadata

Siddhi enables users to identify outliers using linear regression on real time, data streams. The outlier function takes in a dependent event stream (Y), an independent event stream (X) and a user specified range for outliers, and returns whether the current event is an outlier, based on the regression equation that fits historical data.

Input Parameters

Parameter

 Required/Optional

Description

Calculation Interval

Optional

The frequency of regression calculation.

Default value: 1 (i.e. at every event)

Batch Size

Optional

The maximum number of events used for a regression calculation

Default value: 1,000,000,000 events

Confidence Interval

Optional

Confidence Interval to be used for regression calculation

Default value: 0.95

Range

Required

Number of standard deviations from the regression equation

Y Stream

Required

Data stream of the dependent variable

X Stream

Required

Data stream of the independent variable

 

Output Parameters

Parameter

Name

Description

Outlier

outlier

True if the event is an outlier, False if not

Standard Error

stdError

Standard Error of the Regression Equation

β coefficients

beta0, beta1

β coefficients of the Regression Equation

Input Stream Data

Name given in the input stream

All items sent in the input stream

Examples

The following query submits the number of standard deviations to be used as a range (2), a dependent input stream (Y) and an independent input stream X, that will be used to perform linear regression between Y and X and output whether the current event is an outlier or not.

from StockExchangeStream#transform.timeseries:outlier(2, Y, X)

select *

insert into StockForecaster     

 

When executed, the above query will return whether the current event is an outlier or not along with the standard error of the regression equation (ε), β coefficients and all the items available in the input stream. 

  • No labels