Evolving separating references for time series classification

Abstract

The mining of time series data has attracted much attention in the past two decades due to the ubiquity of time series in our daily lives. In particular, classification is perhaps one of the most well-studied topics for time series data. Many state-of-the-art classification techniques work by identifying and extracting patterns or characteristics from the training data, and then applying these patterns or characteristics to classify unlabeled time series. This paper presents a novel finding that sequences of values that are very different from the patterns in the labeled time series can be used as references to classify time series effectively. We propose an evolution process to generate these sequences of values, which we call separating references, from the training data. The proposed method is robust to over-fitting and is especially suitable for the situation where little labeled data is available. We demonstrate that the proposed approach is highly competitive on the well-known UCR time series classification benchmarks.