_pkgdown.yml

template:
  params:
    bootswatch: flatly
    ganalytics: UA-76139189-2

navbar:
  title: "modeltime.gluonts"
  left:
    - icon: fa-home
      href: index.html
    - text: "Getting Started"
      href: articles/getting-started.html
    - text: "Articles"
      href: articles/index.html
      menu:
        - text: "Start Here"
        - text: 'Getting Started'
          href: articles/getting-started.html
        - text: '---'
        - text: "Advanced"
        - text: 'Managing GluonTS Environments'
          href: articles/managing-envs.html
        - text: 'Using GPUs with GluonTS'
          href: articles/using-gpus.html
    - text: "API"
      href: reference/index.html
      menu:
        - text: 'API Functions'
        - icon: fa-home
          text: 'Function Reference'
          href: reference/index.html
        - text: '---'
        - text: 'Change History'
        - text: "News"
          href: news/index.html
    - text: "Ecosystem"
      menu:
        - text: 'Forecast'
        - text: 'Modeltime (Forecasting)'
          href: https://business-science.github.io/modeltime/
        - text: 'TimeTK (Time Series Analysis)'
          href: https://business-science.github.io/timetk/
        - text: "---"
        - text: 'Improve'
        - text: 'Modeltime Ensemble (Blending Forecasts)'
          href: https://business-science.github.io/modeltime.ensemble/
        - text: 'Modeltime Resample (Backtesting)'
          href: https://business-science.github.io/modeltime.resample/
        - text: "---"
        - text: 'Scale'
        - text: 'Modeltime H2O (AutoML)'
          href: https://business-science.github.io/modeltime.h2o/
        - text: 'Modeltime GluonTS (Deep Learning)'
          href: https://business-science.github.io/modeltime.gluonts/
    - icon: fas fa-graduation-cap
      text: "Learn"
      href: https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/

  right:
    - icon: fab fa-github
      href: https://github.com/business-science/modeltime.gluonts

reference:
  - title: Installation
    contents:
    - install_gluonts
  - title: GluonTS Algorithm Integrations
    desc: The main algorithms that have been integrated with modeltime.
    contents:
    - deep_ar
    - nbeats
  - title: Saving and Loading Models
    desc: The `parsnip`-adjacent algorithms that implement time series models.
    contents:
    - save_gluonts_model
  - title: Dials Tuning Parameters
    desc: Tuning parameters for various deep learning models
    contents:
    - nbeats_params
  - title: GluonTS Helpers
    contents:
    - to_gluon_list_dataset
    - as_pandas_timestamp

repo:
  url:
    home: https://github.com/business-science/modeltime.gluonts
    source: https://github.com/business-science/modeltime.gluonts/blob/master/
    issue: https://github.com/business-science/modeltime.gluonts/issues/