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


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Or, download CRAN approved version:

Getting Started

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 tsibble feasts tibbletime
Data Structure tibble (tbl) tsibble (tbl_ts) tsibble (tbl_ts) tibbletime (tbl_time)
Interactive Plots (plotly)
Static Plots (ggplot)
Time Series
Correlation, Seasonality
Anomaly Detection
Data Wrangling
Time-Based Summarization
Time-Based Filtering
Padding Gaps
Low to High Frequency
Sliding / Rolling
Feature Engineering (recipes)
Date Feature Engineering
Holiday Feature Engineering
Fourier Series
Smoothing & Rolling
Cross Validation (rsample)
Time Series Cross Validation
Time Series CV Plan Visualization
More Awesomeness
Making Time Series (Intelligently)
Handling Holidays & Weekends
Class Conversion
Automatic Frequency & Trend

What can you do in 1 line of code?

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…

taylor_30_min %>%
    plot_seasonal_diagnostics(date, value, .interactive = FALSE)

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

taylor_30_min %>%
    plot_acf_diagnostics(date, value, .lags = "1 week", .interactive = FALSE)


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().
  • 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 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().
  • tibbletime (retired): While timetk does not import tibbletime, it uses much of the innovative functionality to interpret time-based phrases:
  • slider: A powerful R package that provides a purrr-syntax for complex rolling (sliding) calculations.
  • padr: Used for padding time series from low frequency to high frequency and filling in gaps.
  • 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.

Learning More


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.

Unlock the High-Performance Time Series Forecasting Course