- 期刊
A Review of Interpretable Classification of Multivariate Time Series
摘要
In recent years, as an important component of data mining, the classification task of MTS has received increasing attention from experts in various fields. Although there have been some research results on MTS classification methods, most of the studies in the literature focus on accuracy as the main research goal, and few of them focus on the interpretability of the results. In this paper, we summarize dozens of MTS classification algorithms with both accuracy and interpretability in three directions, introduce the principles and procedures of these methods, and analyze their advantages and disadvantages. It also provides an outlook on future research directions.
參考文獻
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延伸閱讀
- Hsu, E. Y. (2018). Multivariate Time Series Early Classification with Interpretability using Attention Mechanism [master's thesis, National Chiao Tung University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0030-0205201911135220
- 林志強（2011）。The application of Multiscale entropy in structural changes of time series〔碩士論文，國立臺北大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0023-2707201117400800
- Huang, K. (2016). Model Selection for High-Dimensional Multivariate Time Dependent Models [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201600544
- Yang, Y. H. (2020). Dynamic Selection for Multivariate Time Series with Attention Networks [master's thesis, National Chiao Tung University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0030-1504202111223926
- Gui, J., Zheng, Z., Qin, Z., Jia, D., Gao, Y., Liu, Z., & Yao, Q. (2020). An Approach to Extract State Information from Multivariate Time Series. 電腦學刊, 31(6), 1-11. https://doi.org/10.3966/199115992020123106001