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Related Experiment Video

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Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

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Published on: November 1, 2024

Detecting prediction limit of marked point processes using constrained random shuffle surrogate data.

Yutaka Shimada¹, Kohei Yamamoto², Tohru Ikeguchi²

¹Department of Information and Computer Sciences, Graduate School of Sciences and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan.

Chaos (Woodbury, N.Y.)

|March 23, 2021

Summary

This study introduces a novel method to estimate prediction limits for marked point processes using time-constrained surrogate data. Findings show a correlation between system dynamics and predictability in event data.

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Area of Science:

Complex Systems Analysis
Time Series Data Science
Nonlinear Dynamics

Background:

Marked point processes model discrete events with associated information, common in seismology, neuroscience, and finance.
Understanding the predictability of these processes is crucial for forecasting and system analysis.

Purpose of the Study:

To develop and validate a method for estimating prediction limits in marked point process data.
To explore the relationship between system dynamics, specifically the largest Lyapunov exponent, and the estimated prediction limits.

Main Methods:

Proposed a novel approach using random shuffle surrogate data with time window constraints to estimate prediction limits.
Applied the method to marked point process data from various dynamical systems.
Investigated the correlation between the largest Lyapunov exponent and the estimated prediction limits.

Main Results:

A positive correlation was observed between the reciprocal of the estimated prediction limit and the largest Lyapunov exponent.
The proposed method effectively estimates prediction limits for marked point processes.
The largest Lyapunov exponent serves as an indicator of predictability in these systems.

Conclusions:

The study establishes a quantifiable link between the underlying dynamics of a system and its predictability in the context of marked point processes.
The developed method provides a valuable tool for analyzing and forecasting event-driven time series data.