CRAN/E | modeltime.ensemble

modeltime.ensemble

Ensemble Algorithms for Time Series Forecasting with Modeltime

Installation

About

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability. Refer to papers such as "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) doi:10.3390.

github.com/business-science/modeltime.ensemble
Bug report File report

Key Metrics

Version 1.0.2
R ≥3.5
Published 2022-10-18 528 days ago
Needs compilation? no
License MIT
License File
CRAN checks modeltime.ensemble results

Downloads

Yesterday 30 0%
Last 7 days 130 -32%
Last 30 days 636 -29%
Last 90 days 2.129 +14%
Last 365 days 9.758 -50%

Maintainer

Maintainer

Matt Dancho

mdancho@business-science.io

Authors

Matt Dancho

aut / cre

Business Science

cph

Material

README
NEWS
Reference manual
Package source

In Views

TimeSeries

Vignettes

Getting Started with Modeltime Ensemble
Iterative Forecasting with Nested Ensembles
Autoregressive Forecasting (Recursive Ensembles)

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

modeltime.ensemble archive

Depends

modeltime ≥ 1.2.3
modeltime.resample ≥ 0.2.1
R ≥3.5

Imports

tune ≥ 0.1.2
rsample
yardstick
workflows ≥ 0.2.1
parsnip ≥ 0.1.6
recipes ≥ 0.1.15
timetk ≥ 2.5.0
tibble
dplyr ≥ 1.0.0
tidyr
purrr
glue
stringr
rlang ≥ 0.1.2
cli
generics
magrittr
tictoc
parallel
doParallel
foreach

Suggests

gt
crayon
dials
glmnet
progressr
utils
roxygen2
earth
testthat
tidymodels
xgboost
tidyverse
lubridate
knitr
rmarkdown
covr
qpdf
remotes