README.md

timetk

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Time series analysis in the tidyverse

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 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](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

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:

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

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. You will learn:

Become the Time Series Expert for your organization.

Take the High-Performance Time Series Forecasting Course

Acknowledgements

The timetk package wouldn’t be possible without other amazing time series packages.



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timetk documentation built on April 7, 2022, 5:08 p.m.