_pkgdown.yml

url: https://business-science.github.io/modeltime.ensemble/
template:
  bootstrap: 5
  bootswatch: lux
  params:
    ganalytics: G-20GDZ5LL77

navbar:
  bg: primary
  title: "modeltime.ensemble"
  left:
    - icon: fa-home
      href: index.html
    - text: "Getting Started"
      href: articles/getting-started-with-modeltime-ensemble.html
    - text: "Articles"
      href: articles/index.html
      menu:
        - text: 'Getting Started'
        - text: 'Getting Started with Modeltime Ensemble'
          href: articles/getting-started-with-modeltime-ensemble.html
        - text: "---"
        - text: 'Forecasting Many Time Series'
        - text: 'Nested Forecasting with Ensembles'
          href: articles/nested-ensembles.html
        - text: "---"
        - text: 'Advanced'
        - text: 'Autoregressive Forecasting (Recursive Ensembles)'
          href: articles/recursive-ensembles.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/
    - text: Python
      menu:
      - text: Forecast
      - text: Timetk for Python (Time Series Analysis)
        href: https://business-science.github.io/pytimetk/
    - 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.ensemble

reference:
  - title: Ensemble Forecasting
    desc: Functions used to create ensembles.
    contents:
    - "ensemble_average"
    - "ensemble_weighted"
    - "ensemble_model_spec"
  - title: Nested Ensemble Forecasting
    desc: Make ensembles with the nested forecasting workflow
    contents:
    - "ensemble_nested_average"
    - "ensemble_nested_weighted"



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