Siddhi enables users to forecast future events using linear regression on real time, data streams. The forecast function takes in a dependent event stream (Y), an independent event stream (X) and a user specified next X value, and returns the forecast Y value based on the regression equation of the historical data.
The frequency of regression calculation.
Default value: 1 (i.e. at every event)
The maximum number of events used for a regression calculation
Default value: 1,000,000,000 events
Confidence Interval to be used for regression calculation
Default value: 0.95
Next X Value
Value to be used to forecast the Y value. This can be a constant, or an expression (eg:- x+5)
Data stream of the dependent variable
Data stream of the independent variable
Forecast Y value based on next X and regression equation.
Standard Error of the Regression Equation
β coefficients of the simple linear regression
Input Stream Data
Name given in the input stream
All items sent in the input stream
The following query submits an expression to be used as the next X value (X+2) a dependent input stream (Y) and an independent input stream (X) that will be used to perform linear regression between Y and all X streams, and compute the forecast Y value based on the next X value given by the user.
from StockExchangeStream#transform.timeseries:forecast(X+5, Y, X)
insert into StockForecaster
When executed, the above query will return the forecast Y value based on the regression equation established using the Y stream and the X stream and the standard error of the regression equation (ε), β coefficients and all the items available in the input stream.