  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.
