timetk2.9.0 package

A Tool Kit for Working with Time Series

tk_get_timeseries_variables

Get date or datetime variables (column names)

tk_index

Extract an index of date or datetime from time series objects, models,...

tk_make_future_timeseries

Make future time series from existing

anomalize

Automatic group-wise Anomaly Detection

between_time

Between (For Time Series): Range detection for date or date-time seque...

box_cox_vec

Box Cox Transformation

condense_period

Convert the Period to a Lower Periodicity (e.g. Go from Daily to Month...

diff_vec

Differencing Transformation

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

is_date_class

Check if an object is a date class

lag_vec

Lag Transformation

log_interval_vec

Log-Interval Transformation for Constrained Interval Forecasting

mutate_by_time

Mutate (for Time Series Data)

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

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_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

required_pkgs.timetk

S3 methods for tracking which additional packages are needed for steps...

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_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

summarise_by_time

Summarise (for Time Series Data)

tidyeval

Tidy eval helpers

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

timetk-package

timetk: Time Series Analysis in the Tidyverse

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_lags

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_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_timeseries

Get date features from a time-series index

tk_get_timeseries_unit_frequency

Get the timeseries unit frequency for the primary time scales

tk_make_holiday_sequence

Make daily Holiday and Weekend date sequences

tk_make_timeseries

Intelligent date and date-time sequence creation

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 ...

tk_ts_dispatch_

S3 methods for ts method dispatch

tk_tsfeatures

Time series feature matrix (Tidy)

tk_xts

Coerce time series objects and tibbles with date/date-time columns to ...

tk_zoo

Coerce time series objects and tibbles with date/date-time columns to ...

tk_zooreg

Coerce time series objects and tibbles with date/date-time columns to ...

tk_zooreg_dispatch_

S3 methods for zooreg method dispatch

ts_clean_vec

Replace Outliers & Missing Values in a Time Series

ts_impute_vec

Missing Value Imputation for Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

  • Maintainer: Matt Dancho
  • License: GPL (>= 3)
  • Last published: 2023-10-31