Name
Abstract | ||
|---|---|---|
Over the last decade, huge number of time series stream data are continuously being produced in diverse fields, including finance, signal processing, industry, astronomy and so on. Since time series data has high-dimensional, real-valued, continuous and other related properties, it is of great importance to do dimensionality reduction as a preliminary step. In this paper, we propose a novel online segmentation algorithm based on the importance of TPs to represent the time series into some continuous subsequences and maintain the corresponding local temporal features of the raw time series data. To demonstrate the advantage of our proposed algorithm, we provide extensive experimental results on different kinds of time series datasets for validating our algorithm and comparing it with other baseline methods of online segmentation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-12981-1_33 | CollaborateCom |
Field | DocType | Citations |
Time series,Signal processing,Dimensionality reduction,Computer science,Segmentation,Algorithm,Stream data,Feature based | Conference | 0 |
PageRank | References | Authors |
0.34 | 13 | 6 |