paperGraph

Graphs of our paper in JSS (Journal of Statistical Software)

Penalized parametric change-point detection by functional pruning dynamic programming algorithm. The successive means are constrained using a graph structure with edges defining the nature of the changes These changes can be unconstrained (type std), up or down constrained (type up and down) or constrained by a minimal size jump (type abs). The type null means that the graph allows us to stay on the same segment. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a) for biweight (K) and Huber losses (K and a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). These costs should have a quadratic, log-linear or a log-log representation. This includes quadratic Gaussian cost (type = 'mean'), log-linear cost (type = 'variance', 'poisson' or 'exp') and log-log cost (type = 'negbin'). More details in the paper published in the Journal of Statistical Software: <doi:10.18637/jss.v106.i06>.

Vincent Runge

gfpop

Graph-Constrained Functional Pruning Optimal Partitioning

Toby Hocking

Guillem Rigaill

Daniel Grose

Gaetano Romano

Fatemeh Afghah

Paul Fearnhead

Michel Koskas

Arnaud Liehrmann

paperGraph function

<dl><dt>nb</dt>
<dd>the number of the Figure in paper</dd>
<dt>penalty</dt>
<dd>the penalty to use for change-point</dd>
<dt>decay</dt>
<dd>a nonnegative number to give the strength of the exponential decay into the segment</dd>
<dt>gap</dt>
<dd>a nonnegative number to constrain the size of the gap in the change of state</dd>
<dt>oneValue</dt>
<dd>the value for parameter when we consider the collective anomalies problem</dd>
<dt>K</dt>
<dd>a positive number. Threshold for the Biweight robust loss</dd>
<dt>a</dt>
<dd>a positive number. Slope for the Huber robust loss</dd></dl>

Arguments

Graphs of our paper in JSS — paperGraph

<dl>

<dt>nb</dt>
<dd>the number of the Figure in paper</dd>


<dt>penalty</dt>
<dd>the penalty to use for change-point</dd>


<dt>decay</dt>
<dd>a nonnegative number to give the strength of the exponential decay into the segment</dd>


<dt>gap</dt>
<dd>a nonnegative number to constrain the size of the gap in the change of state</dd>


<dt>oneValue</dt>
<dd>the value for parameter when we consider the collective anomalies problem</dd>


<dt>K</dt>
<dd>a positive number. Threshold for the Biweight robust loss</dd>


<dt>a</dt>
<dd>a positive number. Slope for the Huber robust loss</dd>

</dl>

Graphs of our paper in JSS

paperGraph: Graphs of our paper in JSS

Description

Usage

Value

Arguments