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 | 
| add_time | Add / Subtract (For Time Series) | 
| anomalize | Automatic group-wise Anomaly Detection | 
| auto_lambda | Box Cox Transformation | 
| 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 | 
| condense_period | Convert the Period to a Lower Periodicity (e.g. Go from Daily to Monthly) | 
| diff_inv_vec | Differencing Transformation | 
| diff_vec | Differencing Transformation | 
| 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 | 
| get_tk_time_scale_template | Get and modify the Time Scale Template | 
| has_timetk_idx | Extract an index of date or datetime from time series objects, models, forecasts | 
| is_date_class | Check if an object is a date class | 
| 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 | 
| 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) | 
| normalize_inv_vec | Normalize to Range (0, 1) | 
| normalize_vec | Normalize to Range (0, 1) | 
| 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 | 
| 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) | 
| 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 | 
| 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 | 
| %+time% | Add / Subtract (For Time Series) | 
| %-time% | Add / Subtract (For Time Series) |