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How a series becomes an array a learner reads

Source: R/representation.R
native.Rd

A representation is the reduction the package exists to make explicit: the record unreduced, the record at a calendar grain, several grains bound into one block of features, or a lookback of fixed length ending at each target's own instant. It carries the settings and nothing else, so the same object describes a representation before any record has been seen, names the arm it produced in a fitted object, and rebuilds itself for new targets in predict.timesift().

Usage

native(stats = "mean", year_start = "09-01")

grain(grain, stats = "mean", year_start = "09-01")

multigrain(grains = NULL, stats = "mean", year_start = "09-01")

lookback(span, lag = "0 days", bins = 1L, stats = "mean")

Arguments

stats

Statistics computed per bin, one channel each, in the order given. See grain_matrix() for the seven and for what separates an extreme reading from an extreme day.

year_start

"MM-DD" boundary of the hydrological year, used by "season" and "year".

grain

One of "native", "halfday", "day", "week", "month", "season", "year", or a function of the reading instants returning each reading's bin start. See grain_matrix().

grains

Grains bound side by side into one block, or NULL for the automatic set grains() describes.

span

The lookback's length, as a duration such as "30 days" or a number of seconds. See lookback_matrix() for how a duration is read.

lag

The gap between a target's instant and the end of its lookback.

bins

Sub-bins the lookback is cut into, oldest first. One gives a block of features, several give a sequence.

Value

A timesift_representation.

Details

Every representation carries label, the name it is reported under; kind, one of "grain", "multigrain" and "lookback"; the settings its kind uses; and sequence, which says whether its bins are ordered in time and so mean something to a convolution. native(), grain() and a lookback() of more than one bin are sequences; multigrain() and a one-bin lookback() are blocks of features.

multigrain() flattens each of its grains to one row per target and puts them side by side, so a column of the block names the grain, the statistic and the bin it came from. It is the tabular representation a penalised regression or a random forest reads.

Examples

native()
grain("week", stats = c("cold_day", "mean", "warm_day"))
multigrain(c("month", "season"))
lookback("30 days", bins = 3L)