A Tool Kit for Working with Time Series in R

Documentation for package ‘timetk’ version 2.8.2

Help Pages

-- A --

add_time	Add / Subtract (For Time Series)
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 --

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_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)