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 aremany R packages for working with Time Series data. Here’s howtimetk 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) | ✅ | ❌ | ❌ | ❌ |
| 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 | ✅ | ❌ | ❌ | ❌ |
Summary
Timetk is an amazing package that is part of themodeltime 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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Acknowledgements
Thetimetk package wouldn’t be possible without other amazing time series packages.
stats- Basically everytimetkfunction that uses a period (frequency) argument owes it tots().plot_acf_diagnostics(): Leveragesstats::acf(),stats::pacf()&stats::ccf()plot_stl_diagnostics(): Leveragesstats::stl()
- lubridate:
timetkmakes heavy use offloor_date(),ceiling_date(), andduration()for “time-based phrases”.- Add and Subtract Time (
%+time%&%-time%):"2012-01-01" %+time% "1 month 4 days"useslubridateto intelligently offset the day
- Add and Subtract Time (
- 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 thetidyverts(fable,tsibble,feasts, andfabletools).- The
ts_impute_vec()function for low-level vectorized imputation using STL + Linear Interpolation usesna.interp()under the hood. - The
ts_clean_vec()function for low-level vectorized imputation using STL + Linear Interpolation usestsclean()under the hood. - Box Cox transformation
auto_lambda()usesBoxCox.Lambda().
- The
- tibbletime (retired): While
timetkdoes not importtibbletime, it uses much of the innovative functionality to interpret time-based phrases:tk_make_timeseries()- Extendsseq.Date()andseq.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 basicallyrollify()usingslider(see below).
- slider: A powerful R package that provides a
purrr-syntax for complex rolling (sliding) calculations.slidify()usesslider::pslideunder the hood.slidify_vec()usesslider::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 forpadr::pad(). - See the
step_ts_pad()to apply padding as a preprocessing recipe!
- The
- TSstudio: This is the best interactive time series visualization tool out there. It leverages the
tssystem, which is the same system theforecastR package uses. A ton of inspiration for visuals came from usingTSstudio.
