CRAN/E | forecastML

forecastML

Time Series Forecasting with Machine Learning Methods

Installation

About

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.

github.com/nredell/forecastML/

Key Metrics

Version 0.9.0
R ≥ 3.5.0
Published 2020-05-07 1422 days ago
Needs compilation? no
License MIT
License File
CRAN checks forecastML results

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Maintainer

Maintainer

Nickalus Redell

nickalusredell@gmail.com

Authors

Nickalus Redell

Material

README
Reference manual
Package source

In Views

TimeSeries

Vignettes

Forecast Combination
Customizing Wrapper Functions
Direct Forecasting with Multiple Time Series
Custom Feature Lags
forecastML Overview

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

forecastML archive

Depends

R ≥ 3.5.0
dplyr ≥ 0.8.3

Imports

tidyr ≥ 0.8.1
rlang ≥ 0.4.0
magrittr ≥ 1.5
lubridate ≥ 1.7.4
ggplot2 ≥ 3.1.0
future.apply ≥1.3.0
methods
purrr ≥ 0.3.2
data.table ≥ 1.12.6
dtplyr ≥ 1.0.0
tibble ≥ 2.1.3

Suggests

glmnet ≥ 2.0.16
DT ≥ 0.5
knitr ≥ 1.22
rmarkdown ≥ 1.12.6
xgboost ≥ 0.82.1
randomForest ≥ 4.6.14
testthat ≥ 2.2.1
covr ≥ 3.3.1