create_skeleton

An object of class 'lagged_df' from <code>create_lagged_df(..., type = 'train')</code>.

lagged_df

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

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Nickalus Redell

forecastML

Time Series Forecasting with Machine Learning Methods

create_skeleton function

The output of <code>create_skeleton</code> can be passed into<ul>
<li><code><a rd-options="" href="/link/create_windows?package=forecastML&version=0.9.0" data-mini-rdoc="forecastML::create_windows">create_windows</a></code></li>
</ul>

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

Description

Usage

Arguments

Value

Methods and related functions