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Related Concept Videos

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Related Experiment Video

Updated: Aug 22, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Visibility graph for time series prediction and image classification: a review.

Tao Wen1, Huiling Chen2, Kang Hao Cheong1

  • 1Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), Singapore, 487372 Singapore.

Nonlinear Dynamics
|November 7, 2022
PubMed
Summary

Visibility graph algorithms transform time series and images into complex networks, enabling advanced analysis. These methods show superior performance in time series prediction and image classification compared to existing approaches.

Keywords:
Complex networkImage classificationTime series predictionVisibility graph

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Area of Science:

  • Complex Systems Analysis
  • Data Science
  • Network Science

Background:

  • Time series and image analysis are crucial across various scientific domains.
  • Traditional methods include AI, physical models, and hybrid approaches.
  • Complex networks offer a powerful framework for modeling complex systems.

Purpose of the Study:

  • To review visibility graph algorithms for mapping time series and images to complex networks.
  • To explore how these networks reveal properties of time series and images.
  • To evaluate the efficacy of visibility graph algorithms in prediction and classification tasks.

Main Methods:

  • Development and review of various visibility graph algorithms (e.g., horizontal visibility graph, image visibility graph).
  • Application of statistical physics to analyze network topology and information.
  • Utilizing local random walk algorithms and information fusion for time series forecasting.
  • Employing machine learning models (SVM, LDA) for image classification based on network features.

Main Results:

  • Visibility graph algorithms construct diverse network types (weighted, directed, multi-layered).
  • Network analysis reveals underlying properties of time series and images.
  • Proposed forecasting frameworks and classification methods demonstrate effectiveness.
  • Simulations show visibility graph algorithms outperform existing methods in time series prediction and image classification.

Conclusions:

  • Visibility graph algorithms establish a strong link between complex networks, time series, and image analysis.
  • Complex networks serve as a vital tool for understanding data characteristics.
  • The study highlights the potential and future directions for visibility graph applications.