View source: R/diagnostiscstsfeatures.R
tk_tsfeatures  R Documentation 
tk_tsfeatures()
is a tidyverse compliant wrapper for tsfeatures::tsfeatures()
.
The function computes a matrix of time series features that describes the various time
series. It's designed for groupwise analysis using dplyr
groups.
tk_tsfeatures( .data, .date_var, .value, .period = "auto", .features = c("frequency", "stl_features", "entropy", "acf_features"), .scale = TRUE, .trim = FALSE, .trim_amount = 0.1, .parallel = FALSE, .na_action = na.pass, .prefix = "ts_", .silent = TRUE, ... )
.data 
A 
.date_var 
A column containing either date or datetime values 
.value 
A column containing numeric values 
.period 
The periodicity (frequency) of the time series data. Values can be provided as follows:

.features 
Passed to

.scale 
If 
.trim 
If 
.trim_amount 
Default level of trimming if trim==TRUE. Default: 0.1. 
.parallel 
If TRUE, multiple cores (or multiple sessions) will be used. This only speeds things up when there are a large number of time series. When 
.na_action 
A function to handle missing values. Use na.interp to estimate missing values. 
.prefix 
A prefix to prefix the feature columns. Default: 
.silent 
Whether or not to show messages and warnings. 
... 
Other arguments get passed to the feature functions. 
The timetk::tk_tsfeatures()
function implements the tsfeatures
package
for computing aggregated feature matrix for time series that is useful in many types of
analysis such as clustering time series.
The timetk
version ports the tsfeatures::tsfeatures()
function to a tidyverse
compliant
format that uses a tidy data frame containing grouping columns (optional), a date column, and
a value column. Other columns are ignored.
It then becomes easy to summarize each time series by groupwise application of .features
,
which are simply functions that evaluate a time series and return single aggregated value.
(Example: "mean" would return the mean of the time series (note that values are scaled to mean 1 and sd 0 first))
Function Internals:
Internally, the time series are converted to ts
class using tk_ts(.period)
where the
period is the frequency of the time series. Values can be provided for .period
, which will be used
prior to convertion to ts
class.
The function then leverages tsfeatures::tsfeatures()
to compute the feature matrix of summarized
feature values.
A tibble
or data.frame
with aggregated features that describe each time series.
Rob Hyndman, Yanfei Kang, Pablo MonteroManso, Thiyanga Talagala, Earo Wang, Yangzhuoran Yang, Mitchell O'HaraWild: tsfeatures R package
library(dplyr) library(timetk) walmart_sales_weekly %>% group_by(id) %>% tk_tsfeatures( .date_var = Date, .value = Weekly_Sales, .period = 52, .features = c("frequency", "stl_features", "entropy", "acf_features", "mean"), .scale = TRUE, .prefix = "ts_" )
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