Time Series Classification Method Based on Adaptive Wavelet Decomposition

doi:10.19678/j.issn.1000-3428.0061110

Abstract

Abstract: Time series classification is an important research branch of time series mining, which involves constructing a classification model and modeling the characteristics of the time series.However, most of the existing classification methods only model the time series from the time domain perspective, ignoring its important frequency information.However, a time series usually contains various changing patterns with different changing speeds, which are combined in the time domain.It is difficult to capture the hidden patterns under the combined and complex patterns in the time domain.Considering these issues, this study proposes an Adaptive Multilevel Wavelet Decomposition-based neural Network (AMWDNet) to model the time-frequency information.First, it employs adaptive multilevel wavelet decomposition to extract multilevel time-frequency information, which can decompose a time series into several components from the perspective of both time and frequency domains.Then, it designs a long-term temporal pattern extraction module and a short-term temporal pattern extraction module to model the long-term and short-term temporal patterns, respectively.To evaluate the performance of AMWDNet, this study conducts experiments on eight real-world datasets from UCR Time Series Classification Archive.Based on the results, AMWDNet achieves the highest classification accuracy on seven datasets, exceeding that of the sub-optimal benchmark by 0.1~2.2 percentage point.Its overall classification performance surpasses those of benchmark methods such as MLP and FCN.

Key words: time series classification, adaptive, Wavelet decomposition, multi-resolution analysis, time-frequency analysis

摘要： 时间序列分类即通过构建分类模型建模时间序列中的特征来实现对该时间序列的归类，是时间序列挖掘的重要研究分支。现有的时间序列分类方法多数从时域的角度对时间序列进行建模，忽视了时间序列中隐含的频域信息，而时间序列往往同时蕴含着多种不同变化速率的变化模式，这些变化模式在时域上相互叠加，使得时间序列的变化规律变得比较复杂，因此仅从时域的角度进行建模，难以有效地从复杂的规律中捕获其蕴含的多种相对简单的规律。提出一种基于自适应多级小波分解的神经网络方法AMWDNet，使用自适应小波分解建模时间序列中的多级时频信息，自适应小波分解模块能够同时从时域和频域的角度出发，对时间序列中蕴含的多种变化模式进行有效分解，通过使用长短期时间模式提取模块分别建模时间序列中的长期和短期时间模式。选取时间序列分类任务中8个主流的方法作为基准方法，在UCR数据集仓库中的8个数据集上进行对比实验，结果表明，AMWDNet在其中的7个数据集上取得了最高的分类准确率，相比于次优的基准方法提升了0.1~2.2个百分点，整体分类性能优于MLP和FCN等基准方法。

关键词: 时间序列分类, 自适应, 小波分解, 多分辨率分析, 时频分析

CLC Number:

TP18

LIANG Xiaohui, GUO Shengnan, WAN Huaiyu. Time Series Classification Method Based on Adaptive Wavelet Decomposition[J]. Computer Engineering, 2022, 48(4): 81-88,98.

梁小慧, 郭晟楠, 万怀宇. 基于自适应小波分解的时间序列分类方法[J]. 计算机工程, 2022, 48(4): 81-88,98.

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References

[1] CABALLERO BARAJAS K L, AKELLA R.Dynamically modeling patient's health state from electronic medical records:a time series approach[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York, USA:ACM Press, 2015:69-78.
[2] YU B, YIN H T, ZHU Z X.Spatio-temporal graph convolutional networks:a deep learning framework for traffic forecasting[C]//Proceedings of the 27th IEEE International Joint Conference on Artificial Intelligence.Washington D.C., USA:IEEE Press, 2018:328-339.
[3] KRISTJANPOLLER W, FADIC A, MINUTOLO M C.Volatility forecast using hybrid neural network models[J].Expert Systems with Applications, 2014, 41(5):2437-2442.
[4] YE C, KUMAR B V K V, COIMBRA M T.Heartbeat classification using morphological and dynamic features of ECG signals[J].IEEE Transactions on Bio-Medical Engineering, 2012, 59(10):2930-2941.
[5] 卓勤政.基于深度学习的网络流量分析研究[D].南京:南京理工大学, 2018. ZHUO Q Z.Research on network traffic analysis based on deep learning[D].Nanjing:Nanjing University of Science and Technology, 2018.(in Chinese)
[6] 原继东.时间序列分类算法研究[D].北京:北京交通大学, 2016. YUAN J D.Research on time series classification[D].Beijing:Beijing Jiaotong University, 2016.(in Chinese)
[7] 谭振宁.基于深度学习的时序预测和分类[D].广州:华南理工大学, 2020. TAN Z N.Time series prediction and classification based on deep learning[D].Guangzhou:South China University of Technology, 2020.(in Chinese)
[8] WANG Z G, YAN W Z, OATES T.Time series classification from scratch with deep neural networks:a strong baseline[C]//Proceedings of 2017 International Joint Conference on Neural Networks.Washington D.C., USA:IEEE Press, 2017:1578-1585.
[9] FALOUTSOS C, RANGANATHAN M, MANOLOPOULOS Y.Fast subsequence matching in time-series databases[J].ACM SIGMOD Record, 1994, 23(2):419-429.
[10] SILVA D F, GIUSTI R, KEOGH E, et al.Speeding up similarity search under dynamic time warping by pruning unpromising alignments[J].Data Mining and Knowledge Discovery, 2018, 32(4):988-1016.
[11] LIN J, KHADE R, LI Y.Rotation-invariant similarity in time series using bag-of-patterns representation[J].Journal of Intelligent Information Systems, 2012, 39(2):287-315.
[12] LIN J, KEOGH E, WEI L, et al.Experiencing SAX:a novel symbolic representation of time series[J].Data Mining and Knowledge Discovery, 2007, 15(2):107-144.
[13] SCHÄFER P, HÖGQVIST M.SFA:a symbolic Fourier approximation and index for similarity search in high dimensional datasets[C]//Proceedings of the 15th International Conference on Extending Database Technology.Washington D.C., USA:IEEE Press, 2012:516-527.
[14] YE L X, KEOGH E.Time series shapelets:a new primitive for data mining[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York, USA:ACM Press, 2009:947-956.
[15] LINES J, BAGNALL A.Time series classification with ensembles of elastic distance measures[J].Data Mining and Knowledge Discovery, 2015, 29(3):565-592.
[16] HILLS J, LINES J, BARANAUSKAS E, et al.Classification of time series by shapelet transformation[J].Data Mining and Knowledge Discovery, 2014, 28(4):851-881.
[17] RAKTHANMANON T, KEOGH E.Fast shapelets:a scalable algorithm for discovering time series shapelets[C]//Proceedings of 2013 SIAM International Conference on Data Mining.Philadelphia, USA:Society for Industrial and Applied Mathematics, 2013:668-676.
[18] BAGNALL A, LINES J, HILLS J, et al.Time-series classification with COTE:the collective of transformation-based ensembles[J].IEEE Transactions on Knowledge and Data Engineering, 2015, 27(9):2522-2535.
[19] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need?[EB/OL].[2021-02-10].https://arxivpreprintarxiv:1706.03762.
[20] MALLAT S G.A theory for multiresolution signal decomposition:the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7):674-693.
[21] LI S Y, JIN X Y, XUAN Y, et al.Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting[EB/OL].[2021-02-10].https://arxiv.org/abs/1907.00235.
[22] DAU H A, BAGNALL A, KAMGAR K, et al.The UCR time series archive[J].IEEE/CAA Journal of Automatica Sinica, 2019, 6(6):1293-1305.
[23] SERRÀ J, PASCUAL S, KARATZOGLOU A.Towards a universal neural network encoder for time series[EB/OL].[2021-02-10].https://arxiv.org/abs/1805.03908.
[24] CUI Z C, CHEN W L, CHEN Y X.Multi-scale convolutional neural networks for time series classification[EB/OL].[2021-02-10].https://arxiv.org/abs/160306995.
[25] HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J].Neural Computation, 1997, 9(8):1735-1780.
[26] CHUNG J, GULCEHRE C, CHO K, et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL].[2021-02-10].https://arxivpreprintarxiv:1412.3555.
[27] WANG J Y, WANG Z, LI J F, et al.Multilevel wavelet decomposition network for interpretable time series analysis[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York, USA:ACM Press, 2018:2437-2446.

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