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

Regression

Input Parameters

Output Parameters

Examples