# timetk for R [![R-CMD-check](https://github.com/business-science/timetk/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/timetk/actions/workflows/R-CMD-check.yaml) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/timetk)](https://cran.r-project.org/package=timetk) ![](http://cranlogs.r-pkg.org/badges/timetk?color=brightgreen) ![](http://cranlogs.r-pkg.org/badges/grand-total/timetk?color=brightgreen) [![codecov](https://codecov.io/gh/business-science/timetk/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/timetk) > 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*: ``` r remotes::install_github("business-science/timetk") ``` *Or, download CRAN approved version*: ``` r 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](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: |
## Getting Started - [Visualizing Time Series](https://business-science.github.io/timetk/articles/TK04_Plotting_Time_Series.html) - [Wrangling Time Series](https://business-science.github.io/timetk/articles/TK07_Time_Series_Data_Wrangling.html) - [Full Time Series Machine Learning and Feature Engineering Tutorial](https://business-science.github.io/timetk/articles/TK03_Forecasting_Using_Time_Series_Signature.html) - [API Documentation](https://business-science.github.io/timetk/) for articles and a [complete list of function references](https://business-science.github.io/timetk/reference/index.html). ## 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*](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/) ### 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**](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). 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.

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## 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](https://lubridate.tidyverse.org/): `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](https://github.com/joshuaulrich/xts): Used to calculate periodicity and fast lag automation. - [forecast (retired)](https://pkg.robjhyndman.com/forecast/): 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)](https://business-science.github.io/tibbletime/): 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](https://slider.r-lib.org/): 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](https://edwinth.github.io/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](https://github.com/RamiKrispin/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`.