The VARX object supports regression for Multivariate Time Series data. Given a response matrix y, a predictor matrix x is built that consists of lagged y vectors. Additional future response vectors are built for training.
Create a VARX object from a response matrix. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Create a VARX object from a response matrix. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
whether a backcasted value is prepended to the time series (defaults to false)
the feature/variable names
the maximum forecasting horizon (h = 1 to hh)
the hyper-parameters (defaults to MakeMatrix4TS.hp)
the time range, if relevant (time index may suffice)
the matrix of exogenous variable values
the response/output matrix (multi-variate time series data)
Build the input matrix by combining the p + spec columns for the trend and endogenous variable with the q * xe.dim2 columns for the exogenous variables.
Build the input matrix by combining the p + spec columns for the trend and endogenous variable with the q * xe.dim2 columns for the exogenous variables.
whether a backcasted value is prepended to the time series (defaults to false)
the hyper-parameters
the matrix of exogenous variable values
the matrix vector (time series data)
Form an array of names for the features included in the model.
Form an array of names for the features included in the model.
the hyper-parameters
the number of exogenous variable