train_TRMF: Train a TRMF model
In TRMF: Temporally Regularized Matrix Factorization

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2	## S3 method for class 'TRMF' train(x, numit = 10, ...)

`x`	A TRMF object to be fit.
`numit`	Number of alternating least squares iterations
`...`	ignored

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.

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.

Chad Hammerquist

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

create_TRMF, TRMF_columns, TRMF_trend

# 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)