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_pkgdown.yml
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_pkgdown.yml
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url: https://fable.tidyverts.org
template:
params:
bootswatch: cosmo
includes:
in_header: |
<script defer data-domain="fable.tidyverts.org" src="https://track.mitchelloharawild.com/js/plausible.js"></script>
development:
mode: auto
authors:
Rob Hyndman:
href: http://robjhyndman.com
Mitchell O'Hara-Wild:
href: https://mitchelloharawild.com
Earo Wang:
href: https://earo.me
navbar:
type: default
left:
- text: Reference
href: reference/index.html
- text: Changelog
href: news/index.html
- text: Vignettes
menu:
- text: Introduction to fable
href: articles/fable.html
- text: Forecasting with transformations
href: articles/transformations.html
right:
- text: Feedback
href: https://docs.google.com/forms/d/e/1FAIpQLSfc66U8e8e-x_7TeWpuBAtxqdJD4UXozdkLgmBI3mlRuKPkzA/viewform?usp=sf_link
- icon: fa-github fa-lg
href: https://github.com/tidyverts/fable
reference:
- title: ARIMA
desc: >
The ARIMA model and its supported methods.
contents:
- ends_with("ARIMA")
- unitroot_options
- title: ETS
desc: >
Exponential smoothing state space models.
contents:
- ends_with("ETS")
- title: TSLM
desc: >
Time series linear models.
contents:
- ends_with("TSLM")
- breusch_godfrey
- title: Simple forecasting methods
desc: >
A collection of simple forecasting methods that are commonly used as benchmarks.
contents:
- MEAN
- ends_with("model_mean")
- ends_with("RW")
- NAIVE
- SNAIVE
- title: Neural network autoregression
desc: >
Feed-forward neural networks with a single hidden layer and lagged inputs for forecasting univariate time series.
contents:
- ends_with("NNETAR")
- title: Croston's method
desc: >
Croston's (1972) method for intermittent demand forecasting
contents:
- CROSTON
- ends_with("croston")
- title: Theta method
desc: >
The Theta method of Assimakopoulos and Nikolopoulos (2000)
contents:
- THETA
- ends_with("fable_theta")
- title: Autoregression
desc: >
Autoregressive time series models
contents:
- ends_with("AR")
- title: Vector autoregression
desc: >
Estimates a VAR(p) model with support for exogenous regressors.
contents:
- ends_with("VAR")