Compressed Learning for Time Series Classification

Abstract

The time series classification has been studied for various applications in the last decades. In the time series classification problem, we decide the class information based on a small piece of the time series inputs. In general, the approaches to time series classification can be categorized into three types, distance-based, model-based, and feature-based approaches. In this research, we focus on the feature-based methods, which represent time series as a set of characterized values. It is quite often the case that features generated by existing representation techniques are not transparent to domain experts and the feature that are selected for classification are not completely interpretable. We aim to propose a novel time series representation, called Envelope to solve the problem. The proposed supervised feature extraction method transforms time series into simple 1/0/-1 values. A heuristic is introduced to determine the most appropriate representation which includes the features that are the best to discriminate data of different labels. Moreover, this new representation enjoys the characteristic of sparsity which is an essential property when we need to apply compressed sensing techniques. With this advantage, we can benefit from high transmission efficiency, the reduction of required storage and model complexity. We conduct a series of tests on various benchmark time series data to show the effectiveness of the proposed method. Other than the classification effectiveness, we demonstrate how to visualize the similarity between time series of the same and different kinds from the proposed Envelope method.