An Evaluation of Change Point Detection Algorithms

• URL: http://arxiv.org/abs/2003.06222v3
• Date: Sat, 12 Feb 2022 15:03:25 GMT
• Title: An Evaluation of Change Point Detection Algorithms
• Authors: Gerrit J.J. van den Burg and Christopher K.I. Williams
• Abstract summary: We present a data set specifically designed for the evaluation of change point detection algorithms. Each series was annotated by five human annotators to provide ground truth on the presence and location of change points. Next, we present a benchmark study where 14 algorithms are evaluated on each of the time series in the data set.
• Score: 6.03459316244618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change point detection is an important part of time series analysis, as the presence of a change point indicates an abrupt and significant change in the data generating process. While many algorithms for change point detection have been proposed, comparatively little attention has been paid to evaluating their performance on real-world time series. Algorithms are typically evaluated on simulated data and a small number of commonly-used series with unreliable ground truth. Clearly this does not provide sufficient insight into the comparative performance of these algorithms. Therefore, instead of developing yet another change point detection method, we consider it vastly more important to properly evaluate existing algorithms on real-world data. To achieve this, we present a data set specifically designed for the evaluation of change point detection algorithms that consists of 37 time series from various application domains. Each series was annotated by five human annotators to provide ground truth on the presence and location of change points. We analyze the consistency of the human annotators, and describe evaluation metrics that can be used to measure algorithm performance in the presence of multiple ground truth annotations. Next, we present a benchmark study where 14 algorithms are evaluated on each of the time series in the data set. Our aim is that this data set will serve as a proving ground in the development of novel change point detection algorithms.

Related papers

• Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation [0.0]
  We propose an on-line method for detecting and querying change points in crime-related time series. The method is able to accurately detect change points at very low computational costs.
  arXiv  Detail & Related papers  (2023-12-18T10:49:03Z)
• Unsupervised Change Point Detection for heterogeneous sensor signals [0.0]
  We will exclusively examine unsupervised techniques due to their flexibility in the application to various data sources. The examined methods will be introduced and evaluated based on several criteria to compare the algorithms.
  arXiv  Detail & Related papers  (2023-05-19T19:49:44Z)
• Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation [62.997667081978825]
  We introduce a new approach named FARM - Forward Relevance Aligned Metric. Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series. As a first validation step, we present the application of our FARM approach to synthetic but representative signals.
  arXiv  Detail & Related papers  (2023-04-21T15:22:33Z)
• Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective [67.45111837188685]
  Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data. We experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning.
  arXiv  Detail & Related papers  (2022-06-16T11:44:11Z)
• Deep learning model solves change point detection for multiple change types [69.77452691994712]
  A change points detection aims to catch an abrupt disorder in data distribution. We propose an approach that works in the multiple-distributions scenario.
  arXiv  Detail & Related papers  (2022-04-15T09:44:21Z)
• WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data [4.228718402877829]
  Change point detection methods have the ability to discover changes in an unsupervised fashion. We propose WATCH, a novel Wasserstein distance-based change point detection approach. An extensive evaluation shows that WATCH is capable of accurately identifying change points and outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-18T16:55:29Z)
• Bayesian Online Change Point Detection for Baseline Shifts [0.0]
  In time series data analysis, detecting change points on a real-time basis (online) is of great interest in many areas, such as finance, environmental monitoring, and medicine. One promising means to achieve this is the Bayesian online change point detection (BOCPD) algorithm, which has been successfully adopted in particular cases in which the time series of interest has a fixed baseline. We have found that the algorithm struggles when the baseline irreversibly shifts from its initial state. This is because with the original BOCPD algorithm, the sensitivity with which a change point can be detected is degraded if the data points are fluctuating at locations
  arXiv  Detail & Related papers  (2022-01-07T04:44:25Z)
• Estimating leverage scores via rank revealing methods and randomization [50.591267188664666]
  We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms.
  arXiv  Detail & Related papers  (2021-05-23T19:21:55Z)
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation [69.34035527763916]
  Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal. We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
  arXiv  Detail & Related papers  (2020-08-21T15:03:21Z)
• A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds [54.73161039445703]
  We propose a novel self-training approach that enables a typical object detector trained only with point-level annotations. During training, we utilize the available point annotations to supervise the estimation of the center points of objects. Experimental results show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks.
  arXiv  Detail & Related papers  (2020-07-25T02:14:42Z)
• Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation [1.929039244357139]
  We show how existing algorithms can be generalized using various binary classification and regression models. The algorithms are tested on several synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-01-17T15:45:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.