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Awesome Open Source

timetk

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Time series analysis in the tidyverse

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).

Task timetk tsibble feasts tibbletime
Structure
Data Structure tibble (tbl) tsibble (tbl_ts) tsibble (tbl_ts) tibbletime (tbl_time)
Visualization
Interactive Plots (plotly)
Static Plots (ggplot)
Time Series
Correlation, Seasonality
Data Wrangling
Time-Based Summarization
Time-Based Filtering
Padding Gaps
Low to High Frequency
Imputation
Sliding / Rolling
Machine Learning
Time Series Machine Learning
Anomaly Detection
Clustering
Feature Engineering (recipes)
Date Feature Engineering
Holiday Feature Engineering
Fourier Series
Smoothing & Rolling
Padding
Imputation
Cross Validation (rsample)
Time Series Cross Validation
Time Series CV Plan Visualization
More Awesomeness
Making Time Series (Intelligently)
Handling Holidays & Weekends
Class Conversion
Automatic Frequency & Trend

Getting Started

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

Your probably thinking how am I ever going to learn time series forecasting. Here’s the solution that will save you years of struggling.

Take the High-Performance Forecasting Course

Become the forecasting expert for your organization

High-Performance Time Series Forecasting Course

High-Performance Time Series Course

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.

High-Performance Forecasting Systems will save companies by improving accuracy and scalability. Imagine what will happen to your career if you can provide your organization a “High-Performance Time Series Forecasting System” (HPTSF System).

How to Learn High-Performance Time Series Forecasting

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. You will learn:

  • 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.

Become the Time Series Expert for your organization.


Take the High-Performance Time Series Forecasting Course

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.
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