fable/_pkgdown.yml at master · ErfanHoque49/fable · GitHub

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url: https://fable.tidyverts.org

template:
  params:
    bootswatch: cosmo
    ganalytics: UA-145057858-1

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:
    - ARIMA
    - forecast.ARIMA
    - refit.ARIMA
    - interpolate.ARIMA
    - fitted.ARIMA
    - residuals.ARIMA
- title: ETS
  desc: >
    Exponential smoothing state space models.
  contents:
    - ETS
    - forecast.ETS
    - refit.ETS
    - generate.ETS
    - fitted.ETS
    - residuals.ETS
    - components.ETS
- title: TSLM
  desc: >
    Time series linear models.
  contents:
    - TSLM
    - forecast.TSLM
    - refit.TSLM
    - generate.TSLM
    - interpolate.TSLM
    - fitted.TSLM
    - residuals.TSLM
- title: Simple forecasting methods
  desc: >
    A collection of simple forecasting methods that are commonly used as benchmarks.
  contents:
    - MEAN
    - RW
    - NAIVE
    - SNAIVE
    - forecast.model_mean
    - forecast.RW
    - refit.model_mean
    - generate.model_mean
    - generate.RW
    - fitted.model_mean
    - fitted.RW
    - residuals.model_mean
    - residuals.RW
- title: Neural network autoregression
  desc: >
    Feed-forward neural networks with a single hidden layer and lagged inputs for forecasting univariate time series.
  contents:
    - NNETAR
    - forecast.NNETAR
    - refit.NNETAR
    - generate.NNETAR
    - fitted.NNETAR
    - residuals.NNETAR
- title: Croston's method
  desc: >
    Croston's (1972) method for intermittent demand forecasting
  contents:
    - CROSTON
    - forecast.croston
    - fitted.croston
    - residuals.croston
- title: Theta method
  desc: >
    The Theta method of Assimakopoulos and Nikolopoulos (2000)
  contents:
    - THETA
    - forecast.model_theta
    - fitted.model_theta
    - residuals.mode_theta
- title: Autoregression
  desc: >
    Autoregressive time series models
  contents:
    - AR
    - forecast.AR
    - refit.AR
    - generate.AR
    - fitted.AR
    - residuals.AR
- title: Vector autoregression
  desc: >
    Estimates a VAR(p) model with support for exogenous regressors.
  contents:
    - VAR
    - forecast.VAR
    - fitted.VAR
    - residuals.VAR