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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Change-point detection in time-series data by relative density-ratio estimation.

Song Liu1, Makoto Yamada, Nigel Collier

  • 1Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. song@sg.cs.titech.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society
|March 19, 2013
PubMed
Summary

This study introduces a new statistical method for change-point detection in time-series data. The algorithm accurately identifies abrupt changes using non-parametric divergence estimation, proving effective across diverse datasets.

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Area of Science:

  • Statistics
  • Data Science
  • Time Series Analysis

Background:

  • Change-point detection is crucial for identifying abrupt shifts in time-series data properties.
  • Existing methods may lack accuracy or efficiency in diverse data scenarios.

Purpose of the Study:

  • To develop a novel statistical algorithm for accurate and efficient change-point detection.
  • To leverage non-parametric divergence estimation for identifying property changes in time-series.

Main Methods:

  • Utilized non-parametric divergence estimation between retrospective time-series segments.
  • Employed relative Pearson divergence as the core divergence measure.
  • Applied direct density-ratio estimation for accurate and efficient divergence calculation.

Main Results:

  • Demonstrated the algorithm's usefulness on artificial datasets.
  • Validated the method's effectiveness on real-world data, including human-activity sensing, speech, and Twitter messages.
  • Achieved accurate and efficient change-point detection.

Conclusions:

  • The proposed method offers a robust approach to change-point detection.
  • The algorithm is versatile and applicable to various types of time-series data.
  • This work contributes a valuable tool for analyzing abrupt changes in data streams.