期刊
INFORMATION SCIENCES
卷 450, 期 -, 页码 73-88出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.03.023
关键词
Streaming time-series; Subsequence monitoring; SPRING; NSPRING; FPNS; DTW
资金
- research grant Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data Mining Performance [MYRG2016-00069]
- A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel [FDCT/126/2014/A3]
Streaming time-series has drawn unprecedented interests from the computer science researchers. It requires faster execution time and less memory space than traditional approaches in processing historical time-series. Given the real-time constraint in the analysis over streaming time-series, a proper pre-processing step may not even be applicable. Subsequence monitoring is one of the main functions used in a wide range of time series related applications, e.g. quantitative trading in the stock market. In this paper, we propose a novel approach for multi-subsequence monitoring on streaming time-series. The proposed Forward-propagation NSPRING (FPNS) approach is inspired by the forward propagation mechanism in Artificial Neural Networks (ANN). In our proposed approach the concept of forward propagation is adopted to by-pass the unnecessary calculations as in NSPRING where the whole matrix is computed for the final result. FPNS computes a small part of the matrix by indexing only the necessary calculations with the aid of the forward propagation mechanism. As a result, FPNS can effectively reduce the execution time. In the experiments, we compared the scalability, execution time and memory requirement of FPNS, NSPRING, and UCR-DTW using synthetic and real datasets. The experimental results show that on average, FPNS is about three times faster than NSPRING and one order of magnitude faster than UCR-DTW. In addition, FPNS preserves the same accuracy with NSPRING while FPNS runs much faster than NSPRING. (C) 2018 Elsevier Inc. All rights reserved.
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