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
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The timetk
package wouldn’t be possible without other
amazing time series packages.
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()
timetk
makes heavy use of floor_date()
,
ceiling_date()
, and duration()
for “time-based
phrases”.
%+time%
&
%-time%
):
"2012-01-01" %+time% "1 month 4 days"
uses
lubridate
to intelligently offset the dayts
, and its predecessor is the tidyverts
(fable
, tsibble
, feasts
, and
fabletools
).
ts_impute_vec()
function for low-level vectorized
imputation using STL + Linear Interpolation uses
na.interp()
under the hood.ts_clean_vec()
function for low-level vectorized
imputation using STL + Linear Interpolation uses tsclean()
under the hood.auto_lambda()
uses
BoxCox.Lambda()
.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).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).pad_by_time()
function is a wrapper for
padr::pad()
.step_ts_pad()
to apply padding as a
preprocessing recipe!ts
system, which is the same system the
forecast
R package uses. A ton of inspiration for visuals
came from using TSstudio
.