MGEoT: A Multi-grained Ensemble Method for Time Series Classification

Abstract

Classification of time series has attracted substantial interest over past decades. Methods based on Dynamic Time Warping (DTW), Symbolic Aggregate approXimation (SAX) and Shapelets are widely used and have achieved success in various real-world scenarios. However most existing time series classification methods either focus on global variation (e.g. DTW, SAX) or local variation (e.g. Shapelets). In this paper, we propose a Multi-Grained Ensemble Method for time series classification (MEGoT), which can make use of the variation of multi-grained data at the same time. In MEGoT, unstable base learners (Neural Networks) are assigned different weights to combine the ensemble. Different learners represent the learning features of different subsequences in time series, which can discover the discriminative regions, providing interpretability for classification. The training process of MGEoT is simpler and apt to parallel implementation. In the experiments, we conduct empirical evaluations and comparisons with various existing methods on 25 benchmark datasets. The final results show that dividing samples into smaller granularity is able to improve the diversity of ensemble, and MGEoT is competitive in accuracy under the Nemenyi test. Furthermore, MGEoT can discover the discriminative regions in time series, which may be neglected in the global methods.