Diversified Representation Approach for Time Series Using Storm

With the burgeoning of WE (Internet of Every thin g), massive numbers of loT devices in entensive fields are continuously producing huge number of time series, named as streaming time series (STS). The high dimensionality and dynamic uncertainty of STS lead to the main challenge on traditional time series data mining research. Accordingly, time series representation methods could not only reduce the original high dimensionality of streaming time series, but also contain the main temporal features of raw time series. More importantly, time series representation has been regarded as an necessary preprocessing tool to provide data support for the smooth progress of follow-up research. In this paper, we propose a novel online time series representation approach called continuous segmentation and diversified representation framework (CSDRF) for streaming time series, which contains two different types of time series representation results. The subsequent experiments have been conducted to demonstrate that CSDRF could not only provide the corresponding results to meet the diverse needs of different users, but also provide the corresponding qualified symbolic representation results for time series clustering.