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.
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Siddhi enables users to perform linear regression on real time, data streams. The regress function takes in a dependent event stream (Y), any number of independent event streams (X1, X2,...Xn) and returns all coefficients of the regression equation 

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

Y Stream

Required

Data stream of the dependent variable

X Stream(s)

Required

Data stream(s) of the independent variable

 

Output Parameters

Parameter

Name

Description

Standard Error

stdError

Standard Error of the Regression Equation

β coefficients

beta0, beta1, beta2 etc;

n+1 β coefficients where n is the number of x parameters

Input Stream Data

Name given in the input stream

All attributes sent in the input stream

The regress function will nullify any β coefficients that fail the T-test based on the confidence interval.  The user can access any of the output parameters using the ‘Name’ of the parameter given above.

Examples

The following query submits a calculation interval (every 10 events), a batch size (100,000 events), a confidence interval (0.95), a dependent input stream (Y) and 3 independent input streams (X1, X2, X3) that will be used to perform linear regression between Y and all X streams.

from StockExchangeStream#transform.timeseries:regress(10, 100000, 0.95, Y, X1, X2, X3)

select *

insert into StockForecaster     

 

When executed, the above query will return the standard error of the regression equation (ε), 4 β coefficients (β0, β1, β2, β3) and all the items available in the input stream. Using these results, the user can build a relationship between Y and all Xs (regression equation) as follows 

 

 

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