SCALE-BOSS: A framework for scalable time-series classification using symbolic representations
Apostolos Glenis, George A. Vouros · 2022
Time-Series Classification (TSC) is an important problem in many fields across sciences. Many algorithms for TSC use symbolic representation to combat noise. In this paper we propose a framework, namely SCALE-BOSS, to build TSC algorithms that exploit time-series models based on symbolic representations. While alternative symbolic representations can be incorporated, we have opted to use the Bag-Of-SFA (BOSS) approach, and thus SFA, as a state-of-the-art symbolic time series representation. We investigate the efficiency of several instantiations of this framework based on two main variations, where the TSC model is built either by a time-series classification or by a clustering algorithm. The objective is to advance the computational efficiency of TSC classification algorithms without sacrificing their accuracy. We evaluate the instantiations of the SCALE-BOSS framework on those datasets in the UCR time-series repository that include the largest training sets. Comparisons with state of the art methods on TSC show the balance between computational efficiency and accuracy on predictions achieved.