README.md
In timetk: A Tool Kit for Working with Time Series

timetk for R

Making time series analysis in R easier.

Mission: To make time series analysis in R easier, faster, and more enjoyable.

Download the development version with latest features:

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

Or, download CRAN approved version:

install.packages("timetk")

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](https://business-science.github.io/timetk/) | [tsibble](https://tsibble.tidyverts.org/index.html) | [feasts](https://feasts.tidyverts.org/index.html) | [tibbletime (retired)](https://business-science.github.io/tibbletime/) | |-------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------|-----------------------------------------------------|---------------------------------------------------|------------------------------------------------------------------------| | **Structure** | | | | | | Data Structure | tibble (tbl) | tsibble (tbl_ts) | tsibble (tbl_ts) | tibbletime (tbl_time) | | [**Visualization**](https://business-science.github.io/timetk/articles/TK04_Plotting_Time_Series.html) | | | | | | Interactive Plots (plotly) | ✅ | :x: | :x: | :x: | | Static Plots (ggplot) | ✅ | :x: | ✅ | :x: | | [Time Series](https://business-science.github.io/timetk/articles/TK04_Plotting_Time_Series.html) | ✅ | :x: | ✅ | :x: | | [Correlation, Seasonality](https://business-science.github.io/timetk/articles/TK05_Plotting_Seasonality_and_Correlation.html) | ✅ | :x: | ✅ | :x: | | [**Data Wrangling**](https://business-science.github.io/timetk/articles/TK07_Time_Series_Data_Wrangling.html) | | | | | | 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](https://business-science.github.io/timetk/articles/TK03_Forecasting_Using_Time_Series_Signature.html) | ✅ | :x: | :x: | :x: | | [Anomaly Detection](https://business-science.github.io/timetk/articles/TK08_Automatic_Anomaly_Detection.html) | ✅ | :x: | :x: | :x: | | [Clustering](https://business-science.github.io/timetk/articles/TK09_Clustering.html) | ✅ | :x: | :x: | :x: | | [**Feature Engineering (recipes)**](https://business-science.github.io/timetk/articles/TK03_Forecasting_Using_Time_Series_Signature.html) | | | | | | 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](https://business-science.github.io/timetk/reference/time_series_cv.html) | ✅ | :x: | :x: | :x: | | [Time Series CV Plan Visualization](https://business-science.github.io/timetk/reference/plot_time_series_cv_plan.html) | ✅ | :x: | :x: | :x: | | **More Awesomeness** | | | | | | [Making Time Series (Intelligently)](https://business-science.github.io/timetk/articles/TK02_Time_Series_Date_Sequences.html) | ✅ | ✅ | :x: | ✅ | | [Handling Holidays & Weekends](https://business-science.github.io/timetk/articles/TK02_Time_Series_Date_Sequences.html) | ✅ | :x: | :x: | :x: | | [Class Conversion](https://business-science.github.io/timetk/articles/TK00_Time_Series_Coercion.html) | ✅ | ✅ | :x: | :x: | | [Automatic Frequency & Trend](https://business-science.github.io/timetk/articles/TK06_Automatic_Frequency_And_Trend_Selection.html) | ✅ | :x: | :x: | :x: |

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

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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.