chooseLevel

Select the DWT level of decomposition based on wavelet filter, data series
length and a user choice

The library allows to perform a multivariate time series
classification based on the use of Discrete Wavelet Transform for
feature extraction, a step wise discriminant to select the most
relevant features and finally, the use of a linear or quadratic
discriminant for classification. Note that all these steps can be done
separately which allows to implement new steps. Velasco, I., Sipols,
A., de Blas, C. S., Pastor, L., & Bayona, S. (2023)
<doi:10.1186/S12938-023-01079-X>. Percival, D. B., & Walden, A. T.
(2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014)
<doi:10.1016/j.csda.2013.09.006>.

Iván Velasco

TSEAL

Time Series Analysis Library

chooseLevel function

<dl><dt>choice</dt>
<dd>Valid values:<ul>
<li>"Conservative" : \(J &lt; log_2 ( N / (L - 1) + 1)\)</li>
<li>"Max" : \(J \leq log_2(N)\)</li>
<li>"Supermax" : \( J \leq log_2(1.5 * N)\)</li>
</ul></dd>
<dt>filter</dt>
<dd>Wavelet transform filter name. To see the available filters use
the function <code>availableFilters</code></dd>
<dt>N</dt>
<dd>Number of observations. Must be a positive integer</dd></dl>

Arguments

Select the DWT level of decomposition based on wavelet filter, data series
length and a user choice — chooseLevel

<dl>

<dt>choice</dt>
<dd>Valid values:<ul>
<li>"Conservative" : \(J &lt; log_2 ( N / (L - 1) + 1)\)</li>
<li>"Max" : \(J \leq log_2(N)\)</li>
<li>"Supermax" : \( J \leq log_2(1.5 * N)\)</li>
</ul></dd>


<dt>filter</dt>
<dd>Wavelet transform filter name. To see the available filters use
the function <code>availableFilters</code></dd>


<dt>N</dt>
<dd>Number of observations. Must be a positive integer</dd>

</dl>

chooseLevel: Select the DWT level of decomposition based on wavelet filter, data series length and a user choice

Description

Usage

Value

Arguments

References

Examples