tidyversetoolkit to visualize, wrangle, and transform time series data
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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).
|Data Structure||tibble (tbl)||tsibble (tbl_ts)||tsibble (tbl_ts)||tibbletime (tbl_time)|
|Interactive Plots (plotly)||✅||❌||❌||❌|
|Static Plots (ggplot)||✅||❌||✅||❌|
|Low to High Frequency||✅||❌||❌||❌|
|Sliding / Rolling||✅||✅||❌||✅|
|Feature Engineering (recipes)|
|Date Feature Engineering||✅||❌||❌||❌|
|Holiday Feature Engineering||✅||❌||❌||❌|
|Smoothing & Rolling||✅||❌||❌||❌|
|Cross Validation (rsample)|
|Time Series Cross Validation||✅||❌||❌||❌|
|Time Series CV Plan Visualization||✅||❌||❌||❌|
|Making Time Series (Intelligently)||✅||✅||❌||✅|
|Handling Holidays & Weekends||✅||❌||❌||❌|
|Automatic Frequency & Trend||✅||❌||❌||❌|
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timetk package wouldn’t be possible without other amazing time series packages.
timetkfunction that uses a period (frequency) argument owes it to
timetkmakes heavy use of
duration()for “time-based phrases”.
ts, and it’s predecessor is the
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.
timetkdoes not import
tibbletime, it uses much of the innovative functionality to interpret time-based phrases:
seq.POSIXt()using a simple phase like “2012-02” to populate the entire time series from start to finish in February 2012.
between_time()- Uses innovative endpoint detection from phrases like “2012”
purrr-syntax for complex rolling (sliding) calculations.
tssystem, which is the same system the
forecastR package uses. A ton of inspiration for visuals came from using