Reconstructable and Interpretable Representations for Time Series with Time-Skip Sparse Dictionary Learning
Reconstructable and Interpretable Representations for Time Series with Time-Skip Sparse Dictionary Learning
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DOI：
10.1145/3126686.3126724
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Genta Yoshimura;Atsunori Kanemura;H. Asoh
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文献类型：
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作者：
Genta Yoshimura;Atsunori Kanemura;H. Asoh
将时间序列信号总结为保留信号原始特征的基本模式是一项挑战。良好的汇总允许人们重建原始信号，同时减少的数据大小可以节省存储空间，进而加速后续处理。本文提出了一种时间序列信号的字典学习方法，该方法具有沿时间轴跳过稀疏码的机制，利用时间冗余。所提出的方法给出了时间序列的紧凑且准确的表示。实验结果表明，所提出的方法在信号重建和分类方面实现了低误差，同时减小了表示的大小。由所提出的跳跃机制引起的信号重建误差的退化约为误差幅度的 5%，表示大小减少了约 18 倍。基于所提出的方法的分类准确性始终优于最先进的时间序列字典学习方法。在使用字典学习时，所提出的想法可以是一个有效的选择，字典学习是信号处理的基本技术之一，具有多种应用。
It is challenging to summarize time series signals into essential patterns that preserve the original characteristics of the signals. Good summarization allows one to reconstruct the original signal back while the reduced data size saves storage space and in turn accelerates processing that follows. This paper proposes a dictionary learning method for time series signals with a mechanism of skipping sparse codes along the time axis, utilizing redundancy in time. The proposed method gives compact and accurate representations of time series. Experimental results demonstrate that low errors in both signal reconstruction and classification are achieved by the proposed method while the size of representations is reduced. The degradation of the signal reconstruction errors caused by the proposed skipping mechanism was about 5% of the error magnitude, with about a 18 times fewer representation size. The accuracy of classification based on the proposed methods is always better than the state-of-the-art dictionary learning method for time series. The proposed idea can be an effective option when using dictionary learning, which is one of the fundamental techniques in signal processing and has various applications.