A Novel Segmentation and Representation Approach for Streaming Time Series

Along with the coming of Internet of Everything era, massive numbers of pervasive connected devices in various fields are continuously producing oceans of time series stream data. In order to carry out different kinds of data mining tasks (similarity search, classification, clustering, and prediction) based on streaming time series efficiently and effectively, segmentation and representation which segment a streaming time series into several subsequences and provide approximative representation for the raw data, should be done as the first step. With the virtue of solid theoretical foundations, piecewise linear representation (PLR) has been gained success in yielding more compact representation and fewer segments. However, the current state of art PLR methods have their own flaws: For one thing, most of current PLR methods focus on the guaranteed error bound instead of the holistic approximation error, which may lead to excessive fitting errors of segments and loss of factual research significance. For another, most of current PLR methods process streaming time series with some fixed criteria, which cannot provide a more flexible way to represent streaming time series. Motivated by the above analysis, we propose a novel continuous segmentation and multi-resolution representation approach based on turning points, which subdivides the streaming time series by a set of temporal feature points and represents the time series flexibly. Our method can not only generate more accurate approximation than the state-of-the-art of PLR algorithm, but also represent the streaming time series in a more flexible way to meet different needs of users. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method.