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Related Experiment Video

Updated: Feb 8, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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Published on: November 8, 2019

Multivariate Chaotic Time Series Online Prediction Based on Improved Kernel Recursive Least Squares Algorithm.

Min Han, Shuhui Zhang, Meiling Xu

IEEE Transactions on Cybernetics

|July 12, 2018

Summary

This summary is machine-generated.

This study introduces an improved Kernel Recursive Least Squares (KRLS) algorithm for efficient online prediction of chaotic time series. The enhanced method improves computational efficiency and adapts to changing environments.

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Area of Science:

Machine Learning
Time Series Analysis
Computational Science

Background:

Kernel Recursive Least Squares (KRLS) is a kernel method for time series online prediction.
KRLS offers low computational complexity and recursive updates.
Challenges include increased computational complexity with data size and difficulty adapting to time-varying environments.

Purpose of the Study:

To present an improved KRLS algorithm for multivariate chaotic time series online prediction.
To enhance computational efficiency and adaptivity in time-varying conditions.

Main Methods:

The improved KRLS algorithm combines approximate linear dependency, dynamic adjustment, and coherence criterion with quantization.
This approach addresses the limitations of standard KRLS in handling large datasets and dynamic environments.

Main Results:

The proposed algorithm demonstrates improved computational efficiency during online prediction.
It allows for adaptive weight adjustments in time-varying environments.
Effectiveness is validated on Lorenz, El Nino-Southern Oscillation, sunspots, and Yellow River runoff chaotic time series.

Conclusions:

The improved KRLS algorithm effectively addresses computational complexity and adaptivity issues in chaotic time series prediction.
It offers a robust solution for real-world applications requiring online, adaptive forecasting.