_pkgdown.yml

url: https://business-science.github.io/modeltime/
template:
  bootstrap: 5
  bootswatch: lux
  params:
    ganalytics: G-20GDZ5LL77
navbar:
  bg: primary
  title: modeltime
  left:
  - icon: fa-home
    href: index.html
  - text: Start
    href: articles/getting-started-with-modeltime.html
  - text: Articles
    href: articles/index.html
    menu:
    - text: Getting Started
    - text: Getting Started with Modeltime
      href: articles/getting-started-with-modeltime.html
    - text: '---'
    - text: Forecasting Many Time Series (Scale)
    - text: Global Model Forecasting
      href: articles/modeling-panel-data.html
    - text: Iterative (Nested) Forecasting
      href: articles/nested-forecasting.html
    - text: '---'
    - text: Advanced Topics
    - text: Conformal Prediction Interval Forecasting
      href: articles/modeltime-conformal-prediction.html
    - text: Autoregressive (Recursive) Forecasting
      href: articles/recursive-forecasting.html
    - text: Hyperparameter Tuning & Parallel Processing
      href: articles/parallel-processing.html
    - text: The Modeltime Spark Backend
      href: articles/modeltime-spark.html
    - text: '---'
    - text: Algorithms
    - text: Modeltime Algorithm Roadmap
      href: articles/modeltime-model-list.html
    - text: '---'
    - text: Developers
    - text: Extending Modeltime (Developer Tools)
      href: articles/extending-modeltime.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: R 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
reference:
- title: Modeltime Workflow
  desc: The main workflow functions for time series modeling.
- subtitle: Core Functions
  contents:
  - modeltime_table
  - modeltime_calibrate
  - modeltime_forecast
  - modeltime_accuracy
  - modeltime_refit
  - modeltime_fit_workflowset
- subtitle: Recursive Forecast Prediction
  contents:
  - recursive
  - panel_tail
- subtitle: Plotting & Tables
  contents:
  - starts_with("plot_modeltime")
  - starts_with("table_modeltime")
- subtitle: Residual Analysis
  contents:
  - modeltime_residuals
  - modeltime_residuals_test
  - plot_modeltime_residuals
- title: Nested Forecasting
  desc: Forecast many time series iteratively using "nested modeltime tables". Used
    to apply models to each time series panel independently.
- subtitle: Core functions
  contents:
  - modeltime_nested_fit
  - modeltime_nested_select_best
  - modeltime_nested_refit
  - modeltime_nested_forecast
- subtitle: Extractors
  contents: starts_with("extract_")
- subtitle: Workflow
  contents: extend_timeseries
- title: Algorithms
  desc: The `parsnip`-adjacent algorithms that implement time series models.
- subtitle: Core Forecasting Methods
  desc: These models come with modeltime.
  contents:
  - prophet_reg
  - prophet_boost
  - arima_reg
  - arima_boost
  - exp_smoothing
  - seasonal_reg
  - nnetar_reg
- subtitle: Additional Algorithms
  desc: These algorithms have additional dependencies that can be installed with `dependencies
    = TRUE`
  contents:
  - adam_reg
  - temporal_hierarchy
- subtitle: Baseline Algorithms (Simple Methods)
  contents:
  - window_reg
  - naive_reg
- title: Parallel Processing
  contents:
  - starts_with("parallel_")
  - starts_with("control_")
  - create_model_grid
- title: Modeltime Workflow Helpers
  contents:
  - combine_modeltime_tables
  - add_modeltime_model
  - drop_modeltime_model
  - update_modeltime_model
  - update_modeltime_description
  - pluck_modeltime_model
  - pull_modeltime_residuals
  - pull_parsnip_preprocessor
- title: Accuracy Metrics (Yardstick)
- subtitle: Metric Sets and Summarizers
  contents:
  - default_forecast_accuracy_metric_set
  - summarize_accuracy_metrics
- subtitle: New Accuracy Metrics
  contents:
  - maape
  - maape_vec
- title: Parameters (Dials)
  desc: The `dials` parameter functions that support hyperparameter tuning with `tune`.
- subtitle: General Time Series
  contents: seasonal_period
- subtitle: ARIMA
  contents: starts_with("non_seasonal")
- subtitle: Exponential Smoothing
  contents:
  - error
  - smooth_level
- subtitle: Prophet
  contents: changepoint_num
- subtitle: NNETAR
  contents: num_networks
- subtitle: ADAM
  contents: use_constant
- subtitle: Temporal Hierachical Models
  contents: combination_method
- title: Developer Tools
  desc: Tools for extending `modeltime`.
  contents:
  - new_modeltime_bridge
  - create_xreg_recipe
  - juice_xreg_recipe
  - parse_index_from_data
  - get_model_description
  - get_arima_description
  - get_tbats_description
- title: Data
  contents: starts_with("m750")
repo:
  url:
    home: https://github.com/business-science/modeltime
    source: https://github.com/business-science/modeltime/blob/master/
    issue: https://github.com/business-science/modeltime/issues/