Generalized random shapelet forests

• Published in: Data mining and knowledge discovery Vol. 30; no. 5; pp. 1053 - 1085
• Main Authors: Karlsson, Isak, Papapetrou, Panagiotis, Boström, Henrik
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Computation; Computer and Systems Sciences; Computer Science; Data mining; Data Mining and Knowledge Discovery; data- och systemvetenskap; Datasets; Decision trees; Electrocardiography; Ensemble methods; Forests; Information Storage and Retrieval; Learning; Multivariate time series; Physics; State of the art; Statistics for Engineering; Time series; Time series classification; Time series shapelets; Variables; Multivariate time series; Ensemble methods; Decision trees; Time series classification; Time series shapelets
• ISSN: 1384-5810; 1573-756X; 1573-756X
• DOI: 10.1007/s10618-016-0473-y

Shapelets are discriminative subsequences of time series, usually embedded in shapelet-based decision trees. The enumeration of time series shapelets is, however, computationally costly, which in addition to the inherent difficulty of the decision tree learning algorithm to effectively handle high-dimensional data, severely limits the applicability of shapelet-based decision tree learning from large (multivariate) time series databases. This paper introduces a novel tree-based ensemble method for univariate and multivariate time series classification using shapelets, called the generalized random shapelet forest algorithm. The algorithm generates a set of shapelet-based decision trees, where both the choice of instances used for building a tree and the choice of shapelets are randomized. For univariate time series, it is demonstrated through an extensive empirical investigation that the proposed algorithm yields predictive performance comparable to the current state-of-the-art and significantly outperforms several alternative algorithms, while being at least an order of magnitude faster. Similarly for multivariate time series, it is shown that the algorithm is significantly less computationally costly and more accurate than the current state-of-the-art.