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_pkgdown.yml
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template:
params:
bootswatch: flatly
ganalytics: UA-76139189-2
navbar:
title: "timetk"
left:
- icon: "fa-home"
href: index.html
- text: "Getting Started"
href: articles/TK04_Plotting_Time_Series.html
- text: "Articles"
href: articles/index.html
menu:
- text: 'Visualization'
- text: "Plotting Time Series"
href: articles/TK04_Plotting_Time_Series.html
- text: "Plotting Seasonality and Correlation"
href: articles/TK05_Plotting_Seasonality_and_Correlation.html
- text: "Automatic Anomaly Detection"
href: articles/TK08_Automatic_Anomaly_Detection.html
- text: '---'
- text: "Machine Learning"
- text: "Time Series Machine Learning"
href: articles/TK03_Forecasting_Using_Time_Series_Signature.html
- text: '---'
- text: 'Data Wrangling'
- text: "Time Series Data Wrangling"
href: articles/TK07_Time_Series_Data_Wrangling.html
- text: '---'
- text: 'Time Series Fundamentals'
- text: "What is the Time Series Signature?"
href: articles/TK01_Working_With_Time_Series_Index.html
- text: "Automatic Frequency Detection"
href: articles/TK06_Automatic_Frequency_And_Trend_Selection.html
- text: "---"
- text: "Sequences & Data Structures"
- text: "Intelligent Date & Time Sequences"
href: articles/TK02_Time_Series_Date_Sequences.html
- text: "Time Series Conversion (ts, zoo, & xts)"
href: articles/TK00_Time_Series_Coercion.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: fa-github
href: https://github.com/business-science/timetk
reference:
- title: Plotting Time Series
desc: __Detect relationships through visualizations__
- subtitle: Time Series Plotting
contents:
- "plot_time_series"
- subtitle: Correlation, Seasonalilty, & Anomaly Plotting
contents:
- contains("plot_acf")
- contains("plot_anomaly")
- contains("plot_seasonal")
- contains("plot_stl")
- "plot_time_series_regression"
- title: Time Series Data Wrangling Operations
desc: __Extension for `dplyr` for time-series data manipulations__
- subtitle: Data Frame Operations
contents:
- contains("summarise")
- contains("mutate")
- "pad_by_time"
- "filter_by_time"
- "filter_period"
- "slice_period"
- "condense_period"
- "future_frame"
- subtitle: Function Operations
contents:
- "slidify"
- subtitle: Vector Operations
contents:
- "between_time"
- "add_time"
- title: Augment Operations (Quickly Add Many Features)
desc: __Add multiple columns to the original data. Respects `dplyr` groups.__
contents:
- "tk_augment_timeseries_signature"
- "tk_augment_holiday_signature"
- "tk_augment_slidify"
- "tk_augment_differences"
- "tk_augment_lags"
- starts_with("tk_augment")
- title: Vectorized Transformations
desc: __Use with `mutate` to apply vectorized transformations to time series data__
contents:
- contains("box_cox_vec")
- contains("diff_vec")
- contains("lag_vec")
- "standardize_vec"
- "normalize_vec"
- contains("_vec")
- title: Feature Engineering Operations (Recipe Steps)
desc: __Preprocessing & feature engineering operations for use with `recipes` and the `tidymodels` ecosystem__
- subtitle: Engineered Features
- contents:
- "step_timeseries_signature"
- "step_holiday_signature"
- "step_fourier"
- subtitle: Lags & Diffs
desc: See `recipes::step_lag()` for lagged features.
- contents:
- "step_diff"
- subtitle: Smoothing & Rolling
- contents:
- "step_smooth"
- "step_slidify"
- "step_slidify_augment"
- subtitle: Variance Reduction
desc: See `recipes::step_log()` for log transformation.
- contents:
- "step_box_cox"
- "step_log_interval"
- subtitle: Add Rows to a Time series
contents:
- "step_ts_pad"
- subtitle: Imputation & Outlier Cleaning
desc: See `recipes::step_rollimpute()` for rolling imputation.
- contents:
- "step_ts_impute"
- "step_ts_clean"
- title: Cross Validation Operations (Rsample & Tune)
desc: __Resampling for time series cross validation using `rsamples`__
- subtitle: Time Series Cross Validation (Resample Sets)
contents:
- "time_series_split"
- "time_series_cv"
- subtitle: Cross Validation Plan Visualization (Resample Sets)
desc: Uses the output of `time_series_cv` or `rsample::rolling_origin`
contents:
- "plot_time_series_cv_plan"
- "tk_time_series_cv_plan"
- title: Index Operations
desc: __Extract and check the date or date-time index.__
contents:
- starts_with("tk_index")
- starts_with("has_timetk_idx")
- title: Make Operations
desc: __Make time series sequences.__
contents:
- "tk_make_timeseries"
- starts_with("tk_make_future")
- starts_with("tk_make")
- title: Get Operations
desc: __Get summaries, frequency, and signatures from the time series index.__
contents:
- "tk_get_timeseries_signature"
- "tk_get_holiday_signature"
- contains("tk_get")
- title: Diagnostic Operations
desc: __These power the time series plotting functions__
contents:
- "tk_summary_diagnostics"
- "tk_anomaly_diagnostics"
- "tk_acf_diagnostics"
- "tk_seasonal_diagnostics"
- "tk_stl_diagnostics"
- title: Conversion Operations
desc: __Functions for converting between common time series formats.__
contents:
- "tk_tbl"
- "tk_ts"
- "tk_ts_"
- "tk_xts"
- "tk_xts_"
- "tk_zoo"
- "tk_zoo_"
- "tk_zooreg"
- "tk_zooreg_"
- title: Time Scale Template
desc: __The timescale template is used to automate frequency and trendcycle calculations.__
contents:
- contains("time_scale")
- title: Time Series Datasets
desc: __Time series from various forecasting competitions. Domains include economic, retail, and web (google analytics)__
contents:
- "m4_hourly"
- "m4_daily"
- "m4_weekly"
- contains("m4_")
- contains("walmart")
- contains("wikipedia")
- "bike_sharing_daily"
- "taylor_30_min"
- title: Date Utilities
contents:
- "parse_date2"
- "is_date_class"