A hybrid method for rare time series classification with FastDTW and SBD

LI Xian; NIU Baoning; LIU Haonan; ZHANG Xukang

doi:10.13700/j.bh.1001-5965.2021.0471

Volume 49 Issue 6

Jun. 2023

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Abstract

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Journal of Beijing University of Aeronautics and Astronautics > 2023 > 49(6): 1523-1532.

Sheng Wei, Tan Liwei. Fast data fusion method for MGNC integrated navigation system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(6): 657-660. (in Chinese)

Citation:

LI X，NIU B N，LIU H N，et al. A hybrid method for rare time series classification with FastDTW and SBD[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（6）：1523-1532 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0471

Sheng Wei, Tan Liwei. Fast data fusion method for MGNC integrated navigation system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(6): 657-660. (in Chinese)

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A hybrid method for rare time series classification with FastDTW and SBD

doi: 10.13700/j.bh.1001-5965.2021.0471

College of Information and Computer，Taiyuan University of Technology，Jinzhong 030600，China

Funds: National Natural Science Foundation of China (62072326); Key Research and Development Plan of Shanxi Province (201903D421007); Open Fund of Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology (201903)

More Information

Corresponding author: E-mail：niubaoning@tyut.edu.cn
Received Date: 19 Aug 2021
Accepted Date: 02 Jan 2022
Publish Date: 10 Jan 2022

Abstract

Abstract

Rare time series classification (RTSC) is widely used in astronomical observation and other fields. Aiming at the problems of low accuracy and high time cost in the current rare time series classification methods for large-scale data, RTSC-FS is proposed, which takes the short-time scale rare celestial body light change events in astronomical observations as the research object. The dynamic time wrapping (DTW) enhancement FastDTW and SBD are combined in RTSC-FS to estimate sequence distance. The former has low computational complexity, excellent measurement accuracy, while the latter has fast computational speed. Utilizing additional time-saving data preprocessing methods such as resampling, window function smoothing, standardized data, and sliding window filtering. On the time series data set of magnitude changes recorded by the ground-based wide-angle camera (GWAC), RTSC-FS found 44 curves with flare characteristics from approximately 7.91 million days of light change data. The recall rate is 60.27%, and the precision rate is 34.65%. Compared with the Baseline, the number of discoveries is larger, and the recall rate and accuracy rate have been improved.
- rare time series classification,
- FastDTW algorithm,
- SBD method,
- ground-based wide-angle camera,
- magnitude

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References(35)

References

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