Package: timesift 0.3.1

timesift: Learn Predictive Representations of Time-Varying Data

Fits and compares representations of time-varying data against a prediction target. Given a table of targets and a table of time-stamped series belonging to them, it builds each candidate representation, from the record unreduced through a calendar grain such as a week or a month to a lookback anchored on each target, fits the requested learners on each, scores every candidate on one set of held-out folds, and stacks the out-of-fold predictions into an ensemble. Calendar-aware binning keeps a bin a real week or month rather than a fixed block of hours. Learners, response heads and metrics are registered rather than hard-coded, so adding one is a registration and not a fork of the fitting code. The penalised baseline is an elastic net fitted by cyclic coordinate descent along a warm-started path, following Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>. The shipped default is presence-absence with a joint multi-label head scored by the true skill statistic of Allouche, Tsoar and Kadmon (2006) <doi:10.1111/j.1365-2664.2006.01214.x>, the setting used for species distribution modelling from microclimate loggers.

Authors:Gilles Colling [aut, cre, cph]