CRAN/E | seer

seer

Feature-Based Forecast Model Selection

Installation

About

A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at .

thiyangt.github.io/seer/
Bug report File report

Key Metrics

Version 1.1.8
R ≥ 3.2.3
Published 2022-10-01 545 days ago
Needs compilation? no
License GPL-3
CRAN checks seer results

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Maintainer

Maintainer

Thiyanga Talagala

tstalagala@gmail.com

Authors

Thiyanga Talagala

aut / cre

Rob J Hyndman

ths / aut

George Athanasopoulos

ths / aut

Material

README
Reference manual
Package source

In Views

TimeSeries

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

seer archive

Depends

R ≥ 3.2.3

Imports

stats
urca
forecast ≥ 8.3
dplyr
magrittr
randomForest
forecTheta
stringr
tibble
purrr
future
furrr
utils
tsfeatures

Suggests

testthat ≥ 2.1.0
covr
repmis
knitr
rmarkdown
ggplot2
tidyr
Mcomp
GGally