Train a TRMF model

Description

This function is the "engine" of the TRMF package. It takes a previously created TRMF object and fits it to the data using an alternating least squares algorithm.

Usage

## S3 method for class 'TRMF'
train(x, numit = 10, ...)

Arguments

Details

If a coefficient model is not present in object, it adds a L2 regularization model. If no time series models have been added to object, it adds a simple model using TRMF_simple.

Value

train returns a fitted object of class "TRMF" that contains the data, all added models, matrix factorization and fitted model. The matrix factors Xm, Fm are stored in object$Factors$Xm and object$Factors$Fm respectively. Use fitted to get fitted model, use resid to get residuals, use coef to get coefficients (Fm matrix) and components to get Xm or Fm.

Author(s)

Chad Hammerquist

References

Yu, Hsiang-Fu, Nikhil Rao, and Inderjit S. Dhillon. "High-dimensional time series prediction with missing values." arXiv preprint arXiv:1509.08333 (2015).

See Also

create_TRMF, TRMF_columns, TRMF_trend

Examples

# create test data
xm = poly(x = (-10:10)/10,degree=4)
fm = matrix(rnorm(40),4,10)
Am = xm%*%fm+rnorm(210,0,.2)

# create model
obj = create_TRMF(Am)
out = train(obj)
plot(out)