An Approach to Time Series Classification Using Binary Distribution Tree

As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications. To get rid of costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even better performance compared with state-of-the-art TSC solutions. However, on time series datasets presenting complex patterns, neither 1-Nearest-Neighbour classifier nor deep learning models are capable of achieving satisfactory classification accuracy which motivates us to explore new time series representations to help classifiers further improve the classification accuracy. In this paper, by building the binary distribution tree, an approach to time series classification based on deep learning models using new representations is proposed. By conducting comprehensive experiments over 6 most challenging time series datasets and comparing experimental results of the same classifier using the proposed representation or not, the potential of the proposed approach to enhancing time series classification accuracy is validated with a bunch of helpful findings.