HTS.exponential.smoothing

(Beta verson of) Extends theexponential smoothing of a time
series to a histogram time series,using L2 Wasserstein distance.

In the framework of Symbolic Data Analysis, a relatively new
approach to the statistical analysis of multi-valued data, we consider
histogram-valued data, i.e., data described by univariate histograms. The
methods and the basic statistics for histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
between quantile functions. The package contains unsupervised classification
techniques, least square regression and tools for histogram-valued data and for
histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Antonio Irpino

HistDAWass

Histogram-Valued Data Analysis

HTS.exponential.smoothing function

<dl><dt>HTS</dt>
<dd>A <code>HTS</code> object (a histogram time series).</dd>
<dt>alpha</dt>
<dd>a number between 0 and 1 for exponential smoothing</dd></dl>

Arguments

<dl>
<dt><code>smoothing.alpha</code></dt>
<dd>the alpha parameter</dd></dl>
<dt><code>AveragedHTS</code></dt>
<dd>The smoothed HTS</dd>

Slots

Smoothing with exponential smoothing of a histogram time series — HTS.exponential.smoothing

<dl>

<dt>HTS</dt>
<dd>A <code>HTS</code> object (a histogram time series).</dd>


<dt>alpha</dt>
<dd>a number between 0 and 1 for exponential smoothing</dd>

</dl>

<dl>
<dt><code>smoothing.alpha</code></dt>
<dd>the alpha parameter</dd>


<dt><code>AveragedHTS</code></dt>
<dd>The smoothed HTS</dd>


</dl>

HTS.exponential.smoothing: Smoothing with exponential smoothing of a histogram time series

Description

Usage

Value

Arguments

Slots

Examples