Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation

• URL: http://arxiv.org/abs/2001.06386v1
• Date: Fri, 17 Jan 2020 15:45:38 GMT
• Title: Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation
• Authors: Mikhail Hushchyn and Andrey Ustyuzhanin
• Abstract summary: 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.
• Score: 1.929039244357139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of the change-point detection are based on direct density ratio estimation. In this work we show how existing algorithms can be generalized using various binary classification and regression models. In particular, we show that the Gradient Boosting over Decision Trees and Neural Networks can be used for this purpose. The algorithms are tested on several synthetic and real-world datasets. The results show that the proposed methods outperform classical RuLSIF algorithm. Discussion of cases where the proposed algorithms have advantages over existing methods are also provided.

Related papers

• Ensemble Quadratic Assignment Network for Graph Matching [52.20001802006391]
  Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably. We propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
  arXiv  Detail & Related papers  (2024-03-11T06:34:05Z)
• Discrete Neural Algorithmic Reasoning [21.852775399735005]
  We propose to force neural reasoners to maintain the execution trajectory as a combination of finite predefined states. We get perfect test scores on the SALSA-CLRS benchmark, where we get perfect test scores for all tasks.
  arXiv  Detail & Related papers  (2024-02-18T16:03:04Z)
• Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach [5.408169633844698]
  We propose a change point detection algorithm for time series modeled as neural SDEs. The proposed method jointly learns the unknown change points and the parameters of distinct neural SDE models corresponding to each change point.
  arXiv  Detail & Related papers  (2023-12-20T16:16:29Z)
• Latent Neural Stochastic Differential Equations for Change Point Detection [0.6445605125467574]
  We present a novel change point detection algorithm based on Latent Neural Differential Equations (SDE) Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned processes in different timestamps to find change points of the process.
  arXiv  Detail & Related papers  (2022-08-22T13:53:13Z)
• Scalable computation of prediction intervals for neural networks via matrix sketching [79.44177623781043]
  Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure. This work proposes a new algorithm that can be applied to a given trained neural network and produces approximate prediction intervals.
  arXiv  Detail & Related papers  (2022-05-06T13:18:31Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• 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)
• AP-Loss for Accurate One-Stage Object Detection [49.13608882885456]
  One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously. The former suffers much from extreme foreground-background imbalance due to the large number of anchors. This paper proposes a novel framework to replace the classification task in one-stage detectors with a ranking task.
  arXiv  Detail & Related papers  (2020-08-17T13:22:01Z)
• Multi-Decoder RNN Autoencoder Based on Variational Bayes Method [0.3007949058551534]
  We propose a new clustering algorithm focusing on the generative process of the signal with a recurrent neural network and the variational Bayes method. Our experiments show that the proposed algorithm not only has a robustness against for phase shift, amplitude and signal length variations but also provide a flexible clustering based on the property of the variational Bayes method.
  arXiv  Detail & Related papers  (2020-04-29T08:25:07Z)
• Stochastic batch size for adaptive regularization in deep network optimization [63.68104397173262]
  We propose a first-order optimization algorithm incorporating adaptive regularization applicable to machine learning problems in deep learning framework. We empirically demonstrate the effectiveness of our algorithm using an image classification task based on conventional network models applied to commonly used benchmark datasets.
  arXiv  Detail & Related papers  (2020-04-14T07:54:53Z)
• 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)

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.