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Feature-Based Forecast Model Selection

0packages depend
3.5Kdownloads / year
38.4%test coverage
13/13flavors OK

Overview

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 https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf.

Install

Health

CRAN checks
13OK
Slowest check: 2.6 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-04
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.1.8
READMEYes · 622 wordsVignettesNoNEWSNoCitation fileNoWebsiteYes · pkgdown
Help pages
37
Examples that run
100%1 run in a plain check
Exported functions with examples
3%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
0%

Downloads

3.5K
CRAN downloads in the past year
Rank #8,938 · ~10/day · ~293/mo
Daily download trend is not available in this view yet.
43730 days
1.1K90 days
3.5K1 year
Compare downloads with other packages →
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Repository

77Stars
12Forks
0Open issues
0Open PRs
0Releases
254Commits
2Commit identities
254 commits · Last activity 2022-10-01 · -1.3% stars, 30d
Responsiveness
How these are sampled
PRs merged (all time)
not measured
Median days to close, 50 most recently updated closed issues
11
Median days to close, 50 most recently updated closed PRs
not measured

Stars over time

2024-06-13 · 782026-10-03 · 77
PracticesBeta

8 development-tooling and community-health practices detected across 6 families in the upstream repository

Checks run against github.com/thiyangt/seer on 2026-10-18.

Continuous integration (1)
Travis CI (config file)
Docs source (3)
README.Rmdpkgdown

GitHub Pages site last deployed 2022-10-01

pkgdown site last built 2020-06-01, before release 1.1.8 (2022-10-01)

Coverage (1)
Coverage reporting (Codecov)
CRAN release process (1)
cran-comments.md
Show all practicesHide 2 practices
Lint, format, editor (1)
RStudio project
Git structural (1)
.gitattributes
How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
21 external dependencies (excludes base and recommended)

Installing seer adds 56 packages (32 with compiled code, 40 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, colorspace 2.0 MB.

Depends (1)
R >= 3.2.3
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (3)
Author, Maintainer since 1.1.4 (2020)
Thesis advisor, Author
Thesis advisor, Author
Releases (5)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • 1.1.8Latest
    record
    2022-10-01 · current release · diff ↗
  • R
    R 4.2.0 released · 2022-04-22
  • 1.1.7
    record
    2021-12-08 · diff ↗
  • 1.1.6
    record
    2021-06-01 · diff ↗
  • R
    R 4.1.0 released · 2021-05-18
  • 1.1.5
    record
    2020-06-08 · diff ↗
  • R
    R 4.0.0 released · 2020-04-24
  • 1.1.4
    record
    2020-02-21
  • R
    R 3.6.0 released · 2019-04-26

Package metadata

First published
2020-02-21
Total releases
5 / 6 yrs
License
GPL-3 OSI
Minimum R
≥ 3.2.3
Download size
32 KB
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("seer")
Talagala, T., Athanasopoulos, G., & Hyndman, R. J. (2022). seer: Feature-Based Forecast Model Selection (Version 1.1.8) [Computer software]. https://doi.org/10.32614/CRAN.package.seer

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for seer version 1.1.8 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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