R: A Tool Kit for Working with Time Series

# A Tool Kit for Working with Time Series

## Documentation for package ‘timetk’ version 2.9.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A B C D F G H I L M N P S T W misc

timetk-package | timetk: Time Series Analysis in the Tidyverse

## -- A --

add_time | Add / Subtract (For Time Series)
anomalize | Automatic group-wise Anomaly Detection
auto_lambda | Box Cox Transformation

## -- B --

between_time | Between (For Time Series): Range detection for date or date-time sequences
bike_sharing_daily | Daily Bike Sharing Data
box_cox_inv_vec | Box Cox Transformation
box_cox_vec | Box Cox Transformation

## -- C --

condense_period | Convert the Period to a Lower Periodicity (e.g. Go from Daily to Monthly)

## -- D --

diff_inv_vec | Differencing Transformation
diff_vec | Differencing Transformation

## -- F --

FANG | Stock prices for the "FANG" stocks.
filter_by_time | Filter (for Time-Series Data)
filter_period | Apply filtering expressions inside periods (windows)
fourier_vec | Fourier Series
future_frame | Make future time series from existing

## -- G --

get_tk_time_scale_template | Get and modify the Time Scale Template

## -- H --

has_timetk_idx | Extract an index of date or datetime from time series objects, models, forecasts

## -- I --

is_date_class | Check if an object is a date class

## -- L --

lag_vec | Lag Transformation
lead_vec | Lag Transformation
log_interval_inv_vec | Log-Interval Transformation for Constrained Interval Forecasting
log_interval_vec | Log-Interval Transformation for Constrained Interval Forecasting

## -- M --

m4_daily | Sample of 4 Daily Time Series Datasets from the M4 Competition
m4_hourly | Sample of 4 Hourly 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_weekly | Sample of 4 Weekly Time Series Datasets from the M4 Competition
m4_yearly | Sample of 4 Yearly Time Series Datasets from the M4 Competition
mutate_by_time | Mutate (for Time Series Data)

## -- N --

normalize_inv_vec | Normalize to Range (0, 1)
normalize_vec | Normalize to Range (0, 1)

## -- P --

pad_by_time | Insert time series rows with regularly spaced timestamps
parse_date2 | Fast, flexible date and datetime parsing
parse_datetime2 | Fast, flexible date and datetime parsing
plot_acf_diagnostics | Visualize the ACF, PACF, and CCFs for One or More Time Series
plot_anomalies | Visualize Anomalies for One or More Time Series
plot_anomalies_cleaned | Visualize Anomalies for One or More Time Series
plot_anomalies_decomp | Visualize Anomalies 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 | Interactive Plotting for One or More Time Series
plot_time_series_boxplot | Interactive Time Series Box Plots
plot_time_series_cv_plan | Visualize a Time Series Resample Plan
plot_time_series_regression | Visualize a Time Series Linear Regression Formula

## -- S --

set_tk_time_scale_template | Get and modify the Time Scale Template
slice_period | Apply slice inside periods (windows)
slidify | Create a rolling (sliding) version of any function
slidify_vec | Rolling Window Transformation
smooth_vec | Smoothing Transformation using Loess
standardize_inv_vec | Standardize to Mean 0, Standard Deviation 1 (Center & Scale)
standardize_vec | Standardize to Mean 0, Standard Deviation 1 (Center & Scale)
step_box_cox | Box-Cox Transformation using Forecast Methods
step_diff | Create a differenced predictor
step_fourier | Fourier Features for Modeling Seasonality
step_holiday_signature | Holiday Feature (Signature) Generator
step_log_interval | Log Interval Transformation for Constrained Interval Forecasting
step_slidify | Slidify Rolling Window Transformation
step_slidify_augment | Slidify Rolling Window Transformation (Augmented Version)
step_smooth | Smoothing Transformation using Loess
step_timeseries_signature | Time Series Feature (Signature) Generator
step_ts_clean | Clean Outliers and Missing Data for Time Series
step_ts_impute | Missing Data Imputation for Time Series
step_ts_pad | Pad: Add rows to fill gaps and go from low to high frequency
subtract_time | Add / Subtract (For Time Series)
summarise_by_time | Summarise (for Time Series Data)
summarize_by_time | Summarise (for Time Series Data)

## -- T --

taylor_30_min | Half-hourly electricity demand
tidy.step_box_cox | Box-Cox Transformation using Forecast Methods
tidy.step_diff | Create a differenced predictor
tidy.step_fourier | Fourier Features for Modeling Seasonality
tidy.step_holiday_signature | Holiday Feature (Signature) Generator
tidy.step_log_interval | Log Interval Transformation for Constrained Interval Forecasting
tidy.step_slidify | Slidify Rolling Window Transformation
tidy.step_slidify_augment | Slidify Rolling Window Transformation (Augmented Version)
tidy.step_smooth | Smoothing Transformation using Loess
tidy.step_timeseries_signature | Time Series Feature (Signature) Generator
tidy.step_ts_clean | Clean Outliers and Missing Data for Time Series
tidy.step_ts_impute | Missing Data Imputation for Time Series
tidy.step_ts_pad | Pad: Add rows to fill gaps and go from low to high frequency
timetk | timetk: Time Series Analysis in the Tidyverse
time_arithmetic | Add / Subtract (For Time Series)
time_series_cv | Time Series Cross Validation
time_series_split | Simple Training/Test Set Splitting for Time Series
tk_acf_diagnostics | Group-wise ACF, PACF, and CCF Data Preparation
tk_anomaly_diagnostics | Automatic group-wise Anomaly Detection by STL Decomposition
tk_augment_differences | Add many differenced columns to the data
tk_augment_fourier | Add many fourier series to the data
tk_augment_holiday | Add many holiday features to the data
tk_augment_holiday_signature | Add many holiday features to the data
tk_augment_lags | Add many lags to the data
tk_augment_leads | Add many lags to the data
tk_augment_slidify | Add many rolling window calculations to the data
tk_augment_timeseries | Add many time series features to the data
tk_augment_timeseries_signature | Add many time series features to the data
tk_get_frequency | Automatic frequency and trend calculation from a time series index
tk_get_holiday | Get holiday features from a time-series index
tk_get_holidays_by_year | Get holiday features from a time-series index
tk_get_holiday_signature | Get holiday features from a time-series index
tk_get_timeseries | Get date features from a time-series index
tk_get_timeseries_signature | Get date features from a time-series index
tk_get_timeseries_summary | Get date features 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)
tk_get_trend | Automatic frequency and trend calculation from a time series index
tk_index | Extract an index of date or datetime from time series objects, models, forecasts
tk_make_future_timeseries | Make future time series from existing
tk_make_holiday_sequence | Make daily Holiday and Weekend date sequences
tk_make_timeseries | Intelligent date and date-time sequence creation
tk_make_weekday_sequence | Make daily Holiday and Weekend date sequences
tk_make_weekend_sequence | Make daily Holiday and Weekend date sequences
tk_seasonal_diagnostics | Group-wise Seasonality Data Preparation
tk_stl_diagnostics | Group-wise STL Decomposition (Season, Trend, Remainder)
tk_summary_diagnostics | Group-wise Time Series Summary
tk_tbl | Coerce time-series objects to tibble.
tk_time_scale_template | Get and modify the Time Scale Template
tk_time_series_cv_plan | Time Series Resample Plan Data Preparation
tk_ts | Coerce time series objects and tibbles with date/date-time columns to ts.
tk_tsfeatures | Time series feature matrix (Tidy)
tk_ts_ | Coerce time series objects and tibbles with date/date-time columns to ts.
tk_xts | Coerce time series objects and tibbles with date/date-time columns to xts.
tk_xts_ | Coerce time series objects and tibbles with date/date-time columns to xts.
tk_zoo | Coerce time series objects and tibbles with date/date-time columns to xts.
tk_zooreg | Coerce time series objects and tibbles with date/date-time columns to ts.
tk_zooreg_ | Coerce time series objects and tibbles with date/date-time columns to ts.
tk_zoo_ | Coerce time series objects and tibbles with date/date-time columns to xts.
ts_clean_vec | Replace Outliers & Missing Values in a Time Series
ts_impute_vec | Missing Value Imputation for Time Series

## -- W --

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

## -- misc --

%+time% | Add / Subtract (For Time Series)
%-time% | Add / Subtract (For Time Series)