Evaluating Extended Automata for Sequential Regression
A comparison against standard baselines
J.D. Vonck (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S.E. Verwer – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Kubilay Atasu – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Sequential regression has many real-world applications, including energy consumption forecasting and traffic prediction. In this paper, the use of extended regression automata for sequential regression is investigated. The automata are learned using an MSE-based approach with the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) to score merge and split operations, where the use of BIC is a novel contribution of this work. The approach is evaluated on three datasets and compared against persistence, a regression tree, and a random forest. The automata perform comparably on the small dataset, but on the larger datasets, they do not outperform the regression tree and random forest baselines. This is mainly caused by a bias towards splitting during learning, which prevents the automata from making use of loops and causes them to resemble trees. We further found that BIC produced smaller automata and ran faster, but its accuracy was mixed relative to AIC for our datasets.