The timekit
package contains a collection of tools for working with time series in R. There’s a number of benefits. One of the biggest is the ability to use a time series signature to predict future values (forecast) through data mining techniques. While this post is geared toward exposing the user to the timekit
package, there are examples showing the power of data mining a time series as well as how to work with time series in general. A number of timekit
functions will be discussed and implemented in the post. The first group of functions works with the time series index, and these include functions tk_index()
, tk_get_timeseries_signature()
, tk_augment_timeseries_signature()
and tk_get_timeseries_summary()
. We’ll spend the bulk of this post introducing you to these. The next function deals with creating a future time series from an existing index, tk_make_future_timeseries()
. The last set of functions deal with coercion to and from the major time series classes in R, tk_tbl()
, tk_xts()
, tk_zoo()
(and tk_zooreg()
), and tk_ts()
.