MedianCleaning

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

MedianCleaning function

<dl><dt>timeseries</dt>
<dd>Given time series</dd>
<dt>median_level</dt>
<dd>Median distance between two levels</dd>
<dt>breaks</dt>
<dd>Breaks identified</dd>
<dt>frequency</dt>
<dd>Timeseries frequency, defaults to 12 points</dd></dl>

Arguments

Median level checks — MedianCleaning

<dl>

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


<dt>median_level</dt>
<dd>Median distance between two levels</dd>


<dt>breaks</dt>
<dd>Breaks identified</dd>


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

</dl>

MedianCleaning: Median level checks

Description

Usage

Value

Arguments

Examples