timetk for R

Making time series analysis in R easier.

Mission: To make time series analysis in R easier, faster, and more enjoyable.

Installation

Download the development version with latest features:

remotes::install_github("business-science/timetk")

Or, download CRAN approved version:

install.packages("timetk")

Package Functionality

There are many R packages for working with Time Series data. Here’s how timetk compares to the “tidy” time series R packages for data visualization, wrangling, and feature engineeering (those that leverage data frames or tibbles).

Getting Started

• Visualizing Time Series

• Wrangling Time Series

• Full Time Series Machine Learning and Feature Engineering Tutorial

• API Documentation for articles and a complete list of function references.

Summary

Timetk is an amazing package that is part of the modeltime ecosystem for time series analysis and forecasting. The forecasting system is extensive, and it can take a long time to learn:

• Many algorithms
• Ensembling and Resampling
• Machine Learning
• Deep Learning
• Scalable Modeling: 10,000+ time series

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Time Series is Changing

Time series is changing. Businesses now need 10,000+ time series forecasts every day. This is what I call a High-Performance Time Series Forecasting System (HPTSF) - Accurate, Robust, and Scalable Forecasting.

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• Time Series Machine Learning (cutting-edge) with Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
• Deep Learning with GluonTS (Competition Winners)
• Time Series Preprocessing, Noise Reduction, & Anomaly Detection
• Feature engineering using lagged variables & external regressors
• Hyperparameter Tuning
• Time series cross-validation
• Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
• Scalable Forecasting - Forecast 1000+ time series in parallel
• and more.

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Acknowledgements

The timetk package wouldn’t be possible without other amazing time series packages.

• stats - Basically every timetk function that uses a period (frequency) argument owes it to ts().
  • plot_acf_diagnostics(): Leverages stats::acf(), stats::pacf() & stats::ccf()
  • plot_stl_diagnostics(): Leverages stats::stl()
• lubridate: timetk makes heavy use of floor_date(), ceiling_date(), and duration() for “time-based phrases”.
  • Add and Subtract Time (%+time% & %-time%): "2012-01-01" %+time% "1 month 4 days" uses lubridate to intelligently offset the day
• xts: Used to calculate periodicity and fast lag automation.
• forecast (retired): Possibly my favorite R package of all time. It’s based on ts, and its predecessor is the tidyverts (fable, tsibble, feasts, and fabletools).
  • The ts_impute_vec() function for low-level vectorized imputation using STL + Linear Interpolation uses na.interp() under the hood.
  • The ts_clean_vec() function for low-level vectorized imputation using STL + Linear Interpolation uses tsclean() under the hood.
  • Box Cox transformation auto_lambda() uses BoxCox.Lambda().
    
• tibbletime (retired): While timetk does not import tibbletime, it uses much of the innovative functionality to interpret time-based phrases:
  • tk_make_timeseries() - Extends seq.Date() and seq.POSIXt() using a simple phase like “2012-02” to populate the entire time series from start to finish in February 2012.
  • filter_by_time(), between_time() - Uses innovative endpoint detection from phrases like “2012”
  • slidify() is basically rollify() using slider (see below).
• slider: A powerful R package that provides a purrr-syntax for complex rolling (sliding) calculations.
  • slidify() uses slider::pslide under the hood.
  • slidify_vec() uses slider::slide_vec() for simple vectorized rolls (slides).
• padr: Used for padding time series from low frequency to high frequency and filling in gaps.
  • The pad_by_time() function is a wrapper for padr::pad().
  • See the step_ts_pad() to apply padding as a preprocessing recipe!
• TSstudio: This is the best interactive time series visualization tool out there. It leverages the ts system, which is the same system the forecast R package uses. A ton of inspiration for visuals came from using TSstudio.