Multi-resolution representation with recurrent neural networks application for streaming time series in IoT

Abstract

Nowadays, with the proliferation of IoT (Internet of Things), we have gradually entered into a new IoE (Internet of Everything) era, in which billions of connected devices in widespread fields are constantly producing oceans of streaming time series. In order to conduct in-depth data mining researches (similarity searching, classification, clustering, prediction, etc.) based on streaming time series efficiently and effectively, time series representation should be done as the first step. In this paper, we propose a novel multi-resolution hybrid representation approach for streaming time series, which can not only generate different types of representation results in a more flexible way to meet diverse needs of users, but also be utilized as a useful preprocessing tool for the subsequent time series data mining researches. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method.