create_skeleton: Remove the features from a lagged training dataset to reduce...

In forecastML: Time Series Forecasting with Machine Learning Methods

Description

create_skeleton() strips the feature data from a create_lagged_df() object but keeps the outcome column(s), any grouping columns, and meta-data which allows the resulting lagged_df to be used downstream in the forecastML pipeline. The main benefit is that the custom modeling function passed in train_model() can read data directly from the disk or a database when the dataset is too large to fit into memory.

Usage

create_skeleton(lagged_df)

Arguments

lagged_df

An object of class 'lagged_df' from create_lagged_df(..., type = 'train').

Value

An S3 object of class 'lagged_df' or 'grouped_lagged_df': A list of data.frames with the outcome column(s) and any grouping columns but with all other features removed. A special attribute skeleton = TRUE is added.

Methods and related functions

The output of create_skeleton can be passed into

• create_windows

forecastML documentation built on July 8, 2020, 7:27 p.m.