Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

• URL: http://arxiv.org/abs/2312.11097v1
• Date: Mon, 18 Dec 2023 10:49:03 GMT
• Title: Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation
• Authors: Fabrizio Albertetti, Lionel Grossrieder, Olivier Ribaux, Kilian Stoffel
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Related papers

• Enhancing Changepoint Detection: Penalty Learning through Deep Learning Techniques [2.094821665776961]
  This study introduces a novel deep learning method for predicting penalty parameters. It leads to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets.
  arXiv  Detail & Related papers  (2024-08-01T18:10:05Z)
• Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guarantees [1.6026317505839445]
  We study the online change point detection problem for linear dynamical systems with unknown dynamics. We develop a data-dependent threshold that can be used in our test that allows one to achieve a pre-specified upper bound on the probability of making a false alarm.
  arXiv  Detail & Related papers  (2023-11-30T18:08:16Z)
• 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)
• Learning Sinkhorn divergences for supervised change point detection [24.30834981766022]
  We present a novel change point detection framework that uses true change point instances as supervision for learning a ground metric. Our method can be used to learn a sparse metric which can be useful for both feature selection and interpretation.
  arXiv  Detail & Related papers  (2022-02-08T17:11:40Z)
• Pretrained equivariant features improve unsupervised landmark discovery [69.02115180674885]
  We formulate a two-step unsupervised approach that overcomes this challenge by first learning powerful pixel-based features. Our method produces state-of-the-art results in several challenging landmark detection datasets.
  arXiv  Detail & Related papers  (2021-04-07T05:42:11Z)
• Online Neural Networks for Change-Point Detection [0.6015898117103069]
  We present two online change-point detection approaches based on neural networks. We compare them with the best known algorithms on various synthetic and real world data sets.
  arXiv  Detail & Related papers  (2020-10-03T16:55:59Z)
• 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)
• Offline detection of change-points in the mean for stationary graph signals [55.98760097296213]
  We propose an offline method that relies on the concept of graph signal stationarity. Our detector comes with a proof of a non-asymptotic inequality oracle.
  arXiv  Detail & Related papers  (2020-06-18T15:51:38Z)
• An Evaluation of Change Point Detection Algorithms [6.03459316244618]
  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.
  arXiv  Detail & Related papers  (2020-03-13T12:23:41Z)
• Deep Hough Transform for Semantic Line Detection [70.28969017874587]
  We focus on a fundamental task of detecting meaningful line structures, a.k.a. semantic lines, in natural scenes. Previous methods neglect the inherent characteristics of lines, leading to sub-optimal performance. We propose a one-shot end-to-end learning framework for line detection.
  arXiv  Detail & Related papers  (2020-03-10T13:08:42Z)
• Multi-Task Incremental Learning for Object Detection [71.57155077119839]
  Multi-task learns multiple tasks, while sharing knowledge and computation among them. It suffers from catastrophic forgetting of previous knowledge when learned incrementally without access to the old data.
  arXiv  Detail & Related papers  (2020-02-13T04:58:37Z)

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.