decompose_methods

decompose_twitter

decompose_stl

A <code>tibble</code> or <code>tbl_time</code> object.

data

target

Controls the seasonal adjustment (removal of seasonality).
Input can be either "auto", a time-based definition (e.g. "2 weeks"),
or a numeric number of observations per frequency (e.g. 10).
Refer to <code><a rd-options="=time_frequency" href="/link/time_frequency()?package=anomalize&version=0.2.2&to=%3Dtime_frequency" data-mini-rdoc="=time_frequency::time_frequency()">time_frequency()</a></code>.

frequency

Controls the trend component
For stl, the trend controls the sensitivity of the lowess smoother, which is used to remove the remainder.
For twitter, the trend controls the period width of the median, which are used to remove the trend and center the remainder.

trend

A boolean. If <code>TRUE</code>, will output information related to <code>tbl_time</code> conversions, frequencies,
and trend / median spans (if applicable).

message

Methods that power time_decompose()

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
The main functions are time_decompose(), anomalize(), and time_recompose().
When combined, it's quite simple to decompose time series, detect anomalies,
and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series).
Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
and methods include seasonal decomposition of time series by Loess ("stl") and
seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
and generalized extreme studentized deviation ("gesd"). These methods are based on
those used in the 'forecast' package and the Twitter 'AnomalyDetection' package.
Refer to the associated functions for specific references for these methods.

Matt Dancho

anomalize

Tidy Anomaly Detection

Davis Vaughan

decompose_methods function

<dl><dt>data</dt>
<dd>A <code>tibble</code> or <code>tbl_time</code> object.</dd>
<dt>target</dt>
<dd>A column to apply the function to</dd>
<dt>frequency</dt>
<dd>Controls the seasonal adjustment (removal of seasonality).
Input can be either "auto", a time-based definition (e.g. "2 weeks"),
or a numeric number of observations per frequency (e.g. 10).
Refer to <code>time_frequency()</code>.</dd>
<dt>trend</dt>
<dd>Controls the trend component
For stl, the trend controls the sensitivity of the lowess smoother, which is used to remove the remainder.
For twitter, the trend controls the period width of the median, which are used to remove the trend and center the remainder.</dd>
<dt>message</dt>
<dd>A boolean. If <code>TRUE</code>, will output information related to <code>tbl_time</code> conversions, frequencies,
and trend / median spans (if applicable).</dd></dl>

Arguments

Methods that power time_decompose() — decompose_methods

<dl>

<dt>data</dt>
<dd>A <code>tibble</code> or <code>tbl_time</code> object.</dd>


<dt>target</dt>
<dd>A column to apply the function to</dd>


<dt>frequency</dt>
<dd>Controls the seasonal adjustment (removal of seasonality).
Input can be either "auto", a time-based definition (e.g. "2 weeks"),
or a numeric number of observations per frequency (e.g. 10).
Refer to <code>time_frequency()</code>.</dd>


<dt>trend</dt>
<dd>Controls the trend component
For stl, the trend controls the sensitivity of the lowess smoother, which is used to remove the remainder.
For twitter, the trend controls the period width of the median, which are used to remove the trend and center the remainder.</dd>


<dt>message</dt>
<dd>A boolean. If <code>TRUE</code>, will output information related to <code>tbl_time</code> conversions, frequencies,
and trend / median spans (if applicable).</dd>

</dl>

decompose_methods: Methods that power time_decompose()

Description

Usage

Value

Arguments

References

See Also

Examples