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

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")

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Visualize anomalies…

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

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Make a seasonality plot…

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

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

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

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Acknowledgements

The

timetk

• 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

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.

High-Performance Forecasting Systems will save companies MILLIONS of dollars. Imagine what will happen to your career if you can provide your organization a “High-Performance Time Series Forecasting System” (HPTSF System).

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:

• 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
• 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.

Unlock the High-Performance Time Series Forecasting Course