train.TRMF {TRMF} | R Documentation |
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
x |
A TRMF object to be fit. |
numit |
Number of alternating least squares iterations |
... |
ignored |
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)