Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
arXiv:1811.00620
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.
arXiv commentAn extended version of "Efficient Online Hyperparameter Learning for Traffic Flow Prediction" published in The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC 2018)
References in corpus (7)
- Practical Bayesian Optimization of Machine Learning Algorithms
- Gradient-based Hyperparameter Optimization through Reversible Learning
- Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application
- Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
- Hyperparameter optimization with approximate gradient
- High Dimensional Bayesian Optimisation and Bandits via Additive Models
- Forward and Reverse Gradient-Based Hyperparameter Optimization