To make it easy to ** visualize, wrangle, and feature engineer time series data** for forecasting and machine learning prediction.

*Download the development version with latest features*:

*Or, download CRAN approved version*:

Full Time Series Machine Learning and Feature Engineering Tutorial: Showcases the (NEW)

`step_timeseries_signature()`

for buildingusing*200+ time series features*`parsnip`

,`recipes`

, and`workflows`

.Visit the timetk website documentation for tutorials and a complete list of function references.

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 |
---|---|---|---|---|

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: |

Anomaly Detection | ✅ | :x: | :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: | ✅ |

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: |

Investigate a time series…

```
taylor_30_min %>%
plot_time_series(date, value, .color_var = week(date),
.interactive = FALSE, .color_lab = "Week")
```

Visualize anomalies…

```
walmart_sales_weekly %>%
group_by(Store, Dept) %>%
plot_anomaly_diagnostics(Date, Weekly_Sales,
.facet_ncol = 3, .interactive = FALSE)
```

Make a seasonality plot…

Inspect autocorrelation, partial autocorrelation (and cross correlations too)…

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 it’s 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`

.

*My Talk on High-Performance Time Series Forecasting*

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 MILLIONS of dollars.** Imagine what will happen to your career if you can provide your organization a “High-Performance Time Series Forecasting System” (HPTSF System).

I teach how to build a HPTFS System in my **High-Performance Time Series Forecasting Course**. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:

- Time Series Machine Learning (cutting-edge) with
`Modeltime`

- 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more) - NEW - 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.