scholarly journals Frequent or systematic changes? discussion on “Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection.”

2020 ◽  
Vol 49 (4) ◽  
pp. 1096-1098
Author(s):  
Myung Hwan Seo
Keyword(s):  
Model Selection ◽  
Change Points ◽  
Frequent Change ◽  
Binary Segmentation

scholarly journals Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection—rejoinder

2020 ◽  
Vol 49 (4) ◽  
pp. 1099-1105 ◽  
Author(s):  
Piotr Fryzlewicz
Keyword(s):  
Model Selection ◽  
Change Point ◽  
Complete Solution ◽  
Change Point Detection ◽  
Change Points ◽  
Solution Path ◽  
Frequent Change ◽  
Nested Models ◽  
Binary Segmentation ◽  
Point Detection

AbstractMany existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is “Wild Binary Segmentation 2” (WBS2), a recursive algorithm for producing what we call a ‘complete’ solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing $$0, \ldots , T-1$$ 0 , … , T - 1 change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as “Steepest Drop to Low Levels” (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter.

scholarly journals Discussion of ‘Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection’

2020 ◽  
Vol 49 (4) ◽  
pp. 1076-1080
Author(s):  
Haeran Cho ◽  
Claudia Kirch
Keyword(s):  
Model Selection ◽  
Change Point ◽  
Frequent Change ◽  
New Model ◽  
Interesting Paper ◽  
Selection Step ◽  
Binary Segmentation ◽  
Novel Method ◽  
Low Levels ◽  
Path Ordering

AbstractWe congratulate the author for this interesting paper which introduces a novel method for the data segmentation problem that works well in a classical change point setting as well as in a frequent jump situation. Most notably, the paper introduces a new model selection step based on finding the ‘steepest drop to low levels’ (SDLL). Since the new model selection requires a complete (or at least relatively deep) solution path ordering the change point candidates according to some measure of importance, a new recursive variant of the Wild Binary Segmentation (Fryzlewicz in Ann Stat 42:2243–2281, 2014, WBS) named WBS2, has been proposed for candidate generation.

scholarly journals Detecting multiple generalized change-points by isolating single ones

Metrika ◽  
2021 ◽  
Author(s):  
Andreas Anastasiou ◽  
Piotr Fryzlewicz
Keyword(s):  
Model Selection ◽  
Linear Trend ◽  
Information Criterion ◽  
Change Points ◽  
Isolation Technique ◽  
New Approach ◽  
Piecewise Constant ◽  
Practical Performance ◽  
R Packages ◽  
The Times

AbstractWe introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID’s accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN.

scholarly journals Parametric Methodologies for Detecting Changes in Maximum Temperature of Tlaxco, Tlaxcala, México

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Silvia Herrera Cortés ◽  
Bulmaro Juárez Hernández ◽  
Victor Hugo Vázquez Guevara ◽  
Hugo Adán Cruz Suárez
Keyword(s):  
Linear Time ◽  
Score Test ◽  
Change Points ◽  
Quality Analysis ◽  
Maximum Temperature ◽  
Ratio Test ◽  
Comparison Results ◽  
Binary Segmentation ◽  
Temperature Records ◽  
Sarima Models

In this paper, comparison results of parametric methodologies of change points, applied to maximum temperature records from the municipality of Tlaxco, Tlaxcala, México, are presented. Methodologies considered are likelihood ratio test, score test, and binary segmentation (BS), pruned exact linear time (PELT), and segment neighborhood (SN). In order to compare such methodologies, a quality analysis of the data was performed; in addition, lost data were estimated with linear regression, and finally, SARIMA models were adjusted.

HIERARCHICAL SEGMENTED POINT PROCESS MODELS WITH MULTIPLE CHANGE POINTS FOR MAINTAINED SYSTEMS

2008 ◽  
Vol 15 (03) ◽  
pp. 261-304 ◽  
Author(s):  
A. SYAMSUNDAR ◽  
V. N. A. NAIKAN
Keyword(s):  
Point Process ◽  
Single Point ◽  
Process Models ◽  
Change Points ◽  
Parameter Estimates ◽  
Changing Environment ◽  
Before And After ◽  
Binary Segmentation ◽  
Point Process Models ◽  
The Individual

The failure processes of maintained systems operating in a changing environment may be affected by the changes and exhibit different failure behaviour before and after the changes. Such processes exhibiting abrupt changes in failure intensities at specified times require segmented models with the process domain divided into segments at the points of changes in the environment to represent them. The individual segments can be modeled by any of the usual point process models and combined to form a composite segmented model with multiple change points. This paper proposes such segmented models with multiple change points to represent the failure processes of these systems and uses a hierarchical binary segmentation method to obtain the location of the changes. Its purpose is to quantify the impacts of changes in the environment on the failure intensities. These models are applied to the field data from an industrial setting; parameter estimates obtained and are shown to more accurately describe the failure processes of maintained system in a changing environment than the single point process models usually used. The interpretation and use of these models for maintained systems is also depicted.

scholarly journals A first order autoregressive process with a change point: A bayesian approach based on model selection

2020 ◽  
Vol 15 (3) ◽  
pp. 2395-2412
Author(s):  
Ahmed Hamimes ◽  
Chellai Fatih ◽  
Rachid Benamirouche
Keyword(s):  
Model Selection ◽  
Change Point ◽  
Applied Research ◽  
Bayes Factor ◽  
Autoregressive Process ◽  
Selection Method ◽  
Change Points ◽  
First Order ◽  
Model Selection Method ◽  
The Impact

The change points have considerable effects in different areas of applied research. We will use in this work the pseudo-bayes factor in three autoregressive models of order (1); this method permits to analyse the impact of choice between models and allows the use of a simpler technique with model selection in time series. For application, the monthly fluctuations of the DOW-JONES series between January 1999 and September 2009 have been used; we try to detect the financial crisis between 2007 and 2008 to evaluate the model selection method.

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