Awesome

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).

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

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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 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.

<p class="text-center" style="font-size:24px;"> Become the Time Series Expert for your organization. </p> <br> <p class="text-center" style="font-size:30px;"> <a href="https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting">Take the High-Performance Time Series Forecasting Course</a> </p>

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.