Time series analysis in the
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There are many R packages for working with Time Series data. Here’s
timetk compares to the “tidy” time series R packages for data
visualization, wrangling, and feature engineeering (those that leverage
data frames or tibbles).
Timetk is an amazing package that is part of the
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timetk package wouldn’t be possible without other amazing time
timetkfunction that uses a period (frequency) argument owes it to
timetkmakes heavy use of
duration()for “time-based phrases”.
"2012-01-01" %+time% "1 month 4 days"uses
lubridateto intelligently offset the day
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.
slider::pslideunder the hood.
slider::slide_vec()for simple vectorized rolls (slides).
pad_by_time()function is a wrapper for
step_ts_pad()to apply padding as a preprocessing recipe!
tssystem, which is the same system the
forecastR package uses. A ton of inspiration for visuals came from using
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