data_seatbelts

<p>This is the <code>Seatbelts</code> dataset from the <code>datasets</code> package.</p>

datasets

The purpose of 'forecastML' is to simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped 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

data_seatbelts function

<p>A data.frame with 192 rows and 8 columns:</p>

data_seatbelts: Road Casualties in Great Britain 1969-84

Description

Usage

Arguments

Format