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 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

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