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*:

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

*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 | 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: |

Full Time Series Machine Learning and Feature Engineering Tutorial

API Documentation for articles and a complete list of function references.

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

- 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 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()`

.

- The
- 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!

- The
- 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`

.