Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction

TL;DR

This paper introduces an efficient online hyperparameter optimization method for Kernel Ridge Regression, significantly reducing computation time while maintaining or improving traffic prediction accuracy in real-time applications.

Contribution

The paper proposes a novel online hyperparameter tuning algorithm for Kernel Ridge Regression tailored for traffic time series prediction, enhancing efficiency and accuracy.

Findings

Requires only one-seventh of the computation time of existing methods.

Achieves better or comparable prediction accuracy.

Effective in real-time traffic data scenarios.

Abstract

Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust model parameters automatically based on the data, but often require users to set additional parameters, known as hyperparameters. Hyperparameters can significantly impact prediction accuracy. Traffic measurements, typically collected online by sensors, are serially correlated. Moreover, the data distribution may change gradually. A typical adaptation strategy is periodically re-tuning the model hyperparameters, at the cost of computational burden. In this work, we present an efficient and principled online hyperparameter optimization algorithm for Kernel Ridge regression applied to traffic prediction problems. In tests with real traffic measurement data, our approach requires as little as one-seventh of the computation time of other tuning methods, while achieving better or similar prediction accuracy.