filtered_fold_dt

If the dataset that is going to be folded contains several different time series 
instances of the same process, folding it could introduce false rows with data
from different time series. Given an id variable that labels the different 
instances of a time series inside a dataset and a desired size, this function 
folds the dataset and avoids mixing data from different origins in the same instance.

Learning and inference over dynamic Bayesian networks of arbitrary
Markovian order. Extends some of the functionality offered by the 'bnlearn'
package to learn the networks from data and perform exact inference.
It offers three structure learning algorithms for dynamic Bayesian networks:
Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014)
<doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021)
<doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform
forecasts of arbitrary length. A tool for visualizing the structure of the
net is also provided via the 'visNetwork' package.

David Quesada

dbnR

Dynamic Bayesian Network Learning and Inference

Gabriel Valverde

filtered_fold_dt function

<dl><dt>dt</dt>
<dd>data.table to be folded</dd>
<dt>size</dt>
<dd>the size of the data.table</dd>
<dt>id_var</dt>
<dd>the variable that labels each individual instance of the time series</dd>
<dt>clear_id_var</dt>
<dd>boolean that decides whether or not the id_var column is deleted</dd></dl>

Arguments

Fold a dataset avoiding overlapping of different time series — filtered_fold_dt

<dl>

<dt>dt</dt>
<dd>data.table to be folded</dd>


<dt>size</dt>
<dd>the size of the data.table</dd>


<dt>id_var</dt>
<dd>the variable that labels each individual instance of the time series</dd>


<dt>clear_id_var</dt>
<dd>boolean that decides whether or not the id_var column is deleted</dd>

</dl>

filtered_fold_dt: Fold a dataset avoiding overlapping of different time series

Description

Usage

Value

Arguments

Examples