Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
This work addresses the problem of computational efficiency for traffic prediction systems, offering an incremental improvement over existing tuning methods.
The paper tackled the computational burden of periodically re-tuning hyperparameters for online traffic time series prediction by presenting an efficient online hyperparameter optimization algorithm for Kernel Ridge regression, achieving up to seven times faster computation with similar or better accuracy in real traffic data tests.
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.