Smoothing

Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals.
It is built to perform very well in the presence of significant level shifts. It is designed to play
well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing
and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series
decomposition, breakpoint identification and anomaly detection.

Allen Sunny

StructuralDecompose

Decomposes a Level Shifted Time Series

Smoothing function

<dl><dt>timeseries</dt>
<dd>Given time series</dd>
<dt>frequency</dt>
<dd>Timeseries frequency, defaults to 12 points</dd>
<dt>smoothening_algorithm</dt>
<dd>Smoothening algorithm required</dd>
<dt>breaks</dt>
<dd>Breakpoints identified by the previous algorithm</dd>
<dt>lowess</dt>
<dd>Lowess smoothener</dd></dl>

Arguments

Smoothening of the time series — Smoothing

<dl>

<dt>timeseries</dt>
<dd>Given time series</dd>


<dt>frequency</dt>
<dd>Timeseries frequency, defaults to 12 points</dd>


<dt>smoothening_algorithm</dt>
<dd>Smoothening algorithm required</dd>


<dt>breaks</dt>
<dd>Breakpoints identified by the previous algorithm</dd>


<dt>lowess</dt>
<dd>Lowess smoothener</dd>

</dl>

Smoothing: Smoothening of the time series

Description

Usage

Value

Arguments

Examples