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.

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`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`

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