A Novel Method Based on Data Visual Autoencoding for Time-Series Classification

A variety of techniques based on numerical characteristics are currently presented for mining time-series data. However, we find that time-series data generally contain curves sharing some set of visual characteristics and features. These characteristics offer a deeper understanding of time-series data, and open up a potential new technique for time-series analysis. Particularly beneficial from recent advances in deep neural networks, representations and features can be automatically learnt by deep learning architectures such as autoencoders. Based on that, our work proposes a novel method, named time-series visualization (TSV), to efficiently detect visual characteristics from curves of time-series data and use these characteristics for intelligent analysis. Architecture and algorithm of TSV based on stacked autoencoders are introduced in this paper. Further, important factors affecting the performance of TSV are discussed based on empirical results. Through empirical evaluation, it is demonstrated that TSV has better efficiency and higher classification accuracy on analyzing the datasets with significant curve feature.