Function reference • timetk

Plotting Time Series Detect relationships through visualizations
Time Series Plotting
`plot_time_series()`	Interactive Plotting for One or More Time Series
Correlation, Seasonalilty, & Anomaly Plotting
`plot_acf_diagnostics()`	Visualize the ACF, PACF, and CCFs for One or More Time Series
`plot_anomaly_diagnostics()`	Visualize Anomalies for One or More Time Series
`plot_seasonal_diagnostics()`	Visualize Multiple Seasonality Features for One or More Time Series
`plot_stl_diagnostics()`	Visualize STL Decomposition Features for One or More Time Series
`plot_time_series_regression()`	Visualize a Time Series Linear Regression Formula
Time Series Data Wrangling Operations Extension for `dplyr` for time-series data manipulations
Data Frame Operations
`summarise_by_time()` `summarize_by_time()`	Summarise (for Time Series Data)
`mutate_by_time()`	Mutate (for Time Series Data)
`filter_by_time()`	Filter (for Time-Series Data)
`pad_by_time()`	Insert time series rows with regularly spaced timestamps
`future_frame()`	Make future time series from existing
Function Operations
`slidify()`	Create a rolling (sliding) version of any function
Vector Operations
`between_time()`	Between (For Time Series): Range detection for date or date-time sequences
`add_time()` `subtract_time()` `%+time%` `%-time%`	Add / Subtract (For Time Series)
Vectorized Transformations Use with `mutate` to apply vectorized transformations to time series data
`box_cox_vec()` `box_cox_inv_vec()` `auto_lambda()`	Box Cox Transformation
`diff_vec()` `diff_inv_vec()`	Differencing Transformation
`lag_vec()`	Lag Transformation
`standardize_vec()` `standardize_inv_vec()`	Standardize to Mean 0, Standard Deviation 1 (Center & Scale)
`normalize_vec()` `normalize_inv_vec()`	Normalize to Range (0, 1)
`fourier_vec()`	Fourier Series
`log_interval_vec()` `log_interval_inv_vec()`	Log-Interval Transformation for Constrained Interval Forecasting
`slidify_vec()`	Rolling Window Transformation
`smooth_vec()`	Smoothing Transformation using Loess
`ts_clean_vec()`	Replace Outliers & Missing Values in a Time Series
`ts_impute_vec()`	Missing Value Imputation for Time Series
Augment Operations (Quickly Add Many Features) Add multiple columns to the original data. Respects `dplyr` groups.
`tk_augment_timeseries_signature()`	Add many time series features to the data
`tk_augment_holiday_signature()`	Add many holiday features to the data
`tk_augment_slidify()`	Add many rolling window calculations to the data
`tk_augment_differences()`	Add many differenced columns to the data
`tk_augment_lags()`	Add many lags to the data
`tk_augment_fourier()`	Add many fourier series to the data
Feature Engineering Operations (Recipe Steps) Preprocessing & feature engineering operations for use with `recipes` and the `tidymodels` ecosystem
Engineered Features
`step_timeseries_signature()` `tidy(<step_timeseries_signature>)`	Time Series Feature (Signature) Generator
`step_holiday_signature()` `tidy(<step_holiday_signature>)`	Holiday Feature (Signature) Generator
`step_fourier()` `tidy(<step_fourier>)`	Fourier Features for Modeling Seasonality
Lags & Diffs See `recipes::step_lag()` for lagged features.
`step_diff()` `tidy(<step_diff>)`	Create a differenced predictor
Smoothing & Rolling
`step_smooth()` `tidy(<step_smooth>)`	Smoothing Transformation using Loess
`step_slidify()` `tidy(<step_slidify>)`	Slidify Rolling Window Transformation
`step_slidify_augment()` `tidy(<step_slidify_augment>)`	Slidify Rolling Window Transformation (Augmented Version)
Variance Reduction See `recipes::step_log()` for log transformation.
`step_box_cox()` `tidy(<step_box_cox>)`	Box-Cox Transformation using Forecast Methods
`step_log_interval()` `tidy(<step_log_interval>)`	Log Interval Transformation for Constrained Interval Forecasting
Add Rows to a Time series
`step_ts_pad()` `tidy(<step_ts_pad>)`	Pad: Add rows to fill gaps and go from low to high frequency
Imputation & Outlier Cleaning See `recipes::step_rollimpute()` for rolling imputation.
`step_ts_impute()` `tidy(<step_ts_impute>)`	Missing Data Imputation for Time Series
`step_ts_clean()` `tidy(<step_ts_clean>)`	Clean Outliers and Missing Data for Time Series
Cross Validation Operations (Rsample & Tune) Resampling for time series cross validation using `rsamples`
Time Series Cross Validation (Resample Sets)
`time_series_split()`	Simple Training/Test Set Splitting for Time Series
`time_series_cv()`	Time Series Cross Validation
Cross Validation Plan Visualization (Resample Sets) Uses the output of `time_series_cv` or `rsample::rolling_origin`
`plot_time_series_cv_plan()`	Visualize a Time Series Resample Plan
`tk_time_series_cv_plan()`	Time Series Resample Plan Data Preparation
Index Operations Extract and check the date or date-time index.
`tk_index()` `has_timetk_idx()`	Extract an index of date or datetime from time series objects, models, forecasts
Make Operations Make time series sequences.
`tk_make_timeseries()`	Intelligent date and date-time sequence creation
`tk_make_future_timeseries()`	Make future time series from existing
`tk_make_holiday_sequence()` `tk_make_weekend_sequence()` `tk_make_weekday_sequence()`	Make daily Holiday and Weekend date sequences
Get Operations Get summaries, frequency, and signatures from the time series index.
`tk_get_timeseries_signature()` `tk_get_timeseries_summary()`	Get date features from a time-series index
`tk_get_holiday_signature()` `tk_get_holidays_by_year()`	Get holiday features from a time-series index
`tk_get_frequency()` `tk_get_trend()`	Automatic frequency and trend calculation from a time series index
`tk_get_timeseries_unit_frequency()`	Get the timeseries unit frequency for the primary time scales
`tk_get_timeseries_variables()`	Get date or datetime variables (column names)
Diagnostic Operations These power the time series plotting functions
`tk_summary_diagnostics()`	Group-wise Time Series Summary
`tk_anomaly_diagnostics()`	Automatic group-wise Anomaly Detection by STL Decomposition
`tk_acf_diagnostics()`	Group-wise ACF, PACF, and CCF Data Preparation
`tk_seasonal_diagnostics()`	Group-wise Seasonality Data Preparation
`tk_stl_diagnostics()`	Group-wise STL Decomposition (Season, Trend, Remainder)
Conversion Operations Functions for converting between common time series formats.
`tk_tbl()`	Coerce time-series objects to tibble.
`tk_ts()` `tk_ts_()`	Coerce time series objects and tibbles with date/date-time columns to ts.
`tk_xts()` `tk_xts_()`	Coerce time series objects and tibbles with date/date-time columns to xts.
`tk_zoo()` `tk_zoo_()`	Coerce time series objects and tibbles with date/date-time columns to xts.
`tk_zooreg()` `tk_zooreg_()`	Coerce time series objects and tibbles with date/date-time columns to ts.
Time Scale Template The timescale template is used to automate frequency and trendcycle calculations.
`set_tk_time_scale_template()` `get_tk_time_scale_template()` `tk_time_scale_template()`	Get and modify the Time Scale Template
Time Series Datasets Time series from various forecasting competitions. Domains include economic, retail, and web (google analytics)
`m4_hourly`	Sample of 4 Hourly Time Series Datasets from the M4 Competition
`m4_daily`	Sample of 4 Daily Time Series Datasets from the M4 Competition
`m4_weekly`	Sample of 4 Weekly Time Series Datasets from the M4 Competition
`m4_monthly`	Sample of 4 Monthly Time Series Datasets from the M4 Competition
`m4_quarterly`	Sample of 4 Quarterly Time Series Datasets from the M4 Competition
`m4_yearly`	Sample of 4 Yearly Time Series Datasets from the M4 Competition
`walmart_sales_weekly`	Sample Time Series Retail Data from the Walmart Recruiting Store Sales Forecasting Competition
`wikipedia_traffic_daily`	Sample Daily Time Series Data from the Web Traffic Forecasting (Wikipedia) Competition
`bike_sharing_daily`	Daily Bike Sharing Data
`taylor_30_min`	Half-hourly electricity demand
Date Utilities
`parse_date2()` `parse_datetime2()`	Fast, flexible date and datetime parsing
`is_date_class()`	Check if an object is a date class

Reference

Plotting Time Series

Time Series Plotting

Correlation, Seasonalilty, & Anomaly Plotting

Time Series Data Wrangling Operations

Data Frame Operations

Function Operations

Vector Operations

Vectorized Transformations

Augment Operations (Quickly Add Many Features)

Feature Engineering Operations (Recipe Steps)

Engineered Features

Lags & Diffs

Smoothing & Rolling

Variance Reduction

Add Rows to a Time series

Imputation & Outlier Cleaning

Cross Validation Operations (Rsample & Tune)

Time Series Cross Validation (Resample Sets)

Cross Validation Plan Visualization (Resample Sets)

Index Operations

Make Operations

Get Operations

Diagnostic Operations

Conversion Operations

Time Scale Template

Time Series Datasets

Date Utilities