An experimental analysis on time series transductive classification on graphs

Graph-based semi-supervised learning (SSL) algorithms perform well when the data lie on a low-dimensional manifold. Although these methods achieved satisfactory performance on a variety of domains, they have not been effectively evaluated on time series classification. In this paper, we provide a comprehensive empirical comparison of state-of-the-art graph-based SSL algorithms combined with a variety of graph construction methods in order to evaluate them on time series transductive classification tasks. Through a detailed experimental analysis using recently proposed empirical evaluation models, we show strong and weak points of these classifiers concerning both performance and stability with respect to graph construction and parameter selection. Our results show that some hypotheses raised on previous work do not hold in the time series domain while others may only hold under mild conditions.