timetk

Mission

To make it easy to visualize, wrangle, and feature engineer time series data for forecasting and machine learning prediction.

Installation

Download the development version with latest features:

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

Or, download CRAN approved version:

install.packages("timetk")

Getting Started

• Full Time Series Machine Learning and Feature Engineering Tutorial: Showcases the (NEW) step_timeseries_signature() for building 200+ time series features using parsnip, recipes, and workflows.

• Visit the timetk website documentation for tutorials and a complete list of function references.

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).

What can you do in 1 line of code?

Investigate a time series…

taylor_30_min %>%
    plot_time_series(date, value, .color_var = week(date), 
                     .interactive = FALSE, .color_lab = "Week")

Visualize anomalies…

walmart_sales_weekly %>%
    group_by(Store, Dept) %>%
    plot_anomaly_diagnostics(Date, Weekly_Sales, 
                             .facet_ncol = 3, .interactive = FALSE)

Make a seasonality plot…

taylor_30_min %>%
    plot_seasonal_diagnostics(date, value, .interactive = FALSE)

Inspect autocorrelation, partial autocorrelation (and cross correlations too)…

taylor_30_min %>%
    plot_acf_diagnostics(date, value, .lags = "1 week", .interactive = FALSE)

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 it’s 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.

Learning More

My Talk on High-Performance Time Series Forecasting

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• Time Series Machine Learning (cutting-edge) with Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
• NEW - Deep Learning with GluonTS (Competition Winners)
• Time Series Preprocessing, Noise Reduction, & Anomaly Detection
• Feature engineering using lagged variables & external regressors
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