Build one representation for a set of targets

Turns a representation and the two tables into the [target, bin, channel] array a learner is fitted on. It is the one place the fitting layer builds an array, so timesift() and predict.timesift() reach a record the same way and a candidate refitted on new targets is built from the settings its own arm was.

Usage

build_representation(rep, series, targets, spec)

Arguments

rep

A representation from native(), grain(), multigrain() or lookback().

series

The long table of readings, or NULL where the targets carry the whole predictor block.

targets

The table of prediction targets.

spec

The resolved settings timesift() carries: the identifier, time, value, anchor and static columns it settled on.

Value

A timesift_matrix.

Details

The rows are the targets, in the order the fitting layer keeps them: sorted by identifier where one target row belongs to each unit, and in the targets' own order where target_time anchors them. Columns named in static are appended as channels holding one value per target, constant across the bins.