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Updated: Jan 30, 2026

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Nonlinear information data mining based on time series for fractional differential operators.

Shaofei Wu1

  • 1Hubei Province Key Laboratory of Intelligent Robots, Wuhan Institute of Technology, Wuhan 430000, People's Republic of China and School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430000, People's Republic of China.

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Summary

This study applies fractional calculus and time series analysis to public security intelligence, enhancing information extraction and future trend prediction for more credible case forecasting.

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Area of Science:

  • * Mathematical analysis and modeling
  • * Public security intelligence analysis
  • * Information science and systems

Background:

  • * Mathematical methods are crucial for advancing scientific disciplines.
  • * Classical calculus has limitations in modeling complex natural systems, particularly in information processing.
  • * Fractional-order system models offer superior performance for complex system characterization.

Purpose of the Study:

  • * To integrate time series analysis with fractional calculus for public security intelligence.
  • * To develop a mathematical model for analyzing network intelligence and predicting future events.
  • * To validate the predictive accuracy of the proposed model against actual data.

Main Methods:

  • * Application of time series analysis within the public security intelligence framework.
  • * Construction of a mathematical model utilizing fractional differential operators.
  • * Analysis of network intelligence data to forecast case occurrences.
  • * Comparative validation of predicted versus actual data.

Main Results:

  • * The proposed fractional-order model effectively analyzes network intelligence.
  • * The model demonstrates predictive capabilities for future case occurrences.
  • * Validation confirms the credibility and accuracy of the predictive results.

Conclusions:

  • * Fractional calculus provides a more robust approach for complex system modeling in intelligence analysis.
  • * The integration of time series analysis and fractional operators enhances predictive accuracy.
  • * This methodology offers a valuable tool for improving public security intelligence and forecasting.