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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (7): 1354-1364    DOI: 10.3785/j.issn.1008-973X.2023.07.010
自动化技术     
时序基因驱动的特征表示模型
黄建平1(),陈可2,张建松1,沈思琪1
1. 国网浙江省电力有限公司，浙江 杭州 310063
2. 国网浙江省电力有限公司信息通信分公司，浙江 杭州 310016
Time-series gene driven feature representation model
Jian-ping HUANG1(),Ke CHEN2,Jian-song ZHANG1,Si-qi SHEN1
1. Net Zhejiang Electric Power Limited Company, Hangzhou 310063, China
2. Information Communication Branch, Net Zhejiang Electric Power Limited Company, Hangzhou 310016, China
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摘要：

定义“演变基因”的概念来捕获时间序列所隐含的用户行为，描述这些行为如何导致时间序列的产生. 提出统一的框架，通过学习分类器来识别片段的不同演变基因，采用对抗性生成器估计片段的分布来实现演变基因. 该模型有3个主要组成部分：基因识别，旨在学习片段的相应基因；基因生成，旨在学习从基因中生成片段；基因应用，旨在建模行为演变，将学习到的基因应用于未来值和事件的预测中. 本研究的实验基于1个合成数据集和5个真实数据集，相关结果表明，该方法不仅可以获得好的预测结果，而且能够提供对结果的有效解释.
关键词： 时间序列;  演变基因;  生成模型;  对抗性生成器;  特征学习    
Abstract:

The concept of "evolutionary genes" was defined to capture the underlying user behaviors in time series and describe how these behaviors lead to the generation of time series. A unified framework was proposed. A classifier was learned to identify different evolutionary genes of segments, and an adversarial generator was adopted to estimate the distribution of segments for evolutionary genes. The model consists of three main components: gene identification which aims at learning the corresponding genes of segments; gene generation which aims at learning to generate segments from genes; gene application which aims at modeling behavioral evolution and applying the learned genes to predict future values and events. The experiments of this study were based on one synthetic dataset and five real datasets. Results demonstrate that the method not only achieves good prediction results, but also provides effective explanations for the results.
Key words: time series    evolutionary gene    generation model    adversarial generator    representation learning
收稿日期: 2022-07-11 出版日期: 2023-07-17
CLC:  TP 399  
作者简介: 黄建平（1972—），男，高级工程师，从事数据治理、企业运营管理、数据分析应用、大数据、云计算、人工智能、机电工程的研究. orcid.org/0000-0002-2319-1968. E-mail： huang_jianping@zj.sgcc.com.cn
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引用本文:

黄建平,陈可,张建松,沈思琪. 时序基因驱动的特征表示模型[J]. 浙江大学学报(工学版), 2023, 57(7): 1354-1364.

Jian-ping HUANG,Ke CHEN,Jian-song ZHANG,Si-qi SHEN. Time-series gene driven feature representation model. Journal of ZheJiang University (Engineering Science), 2023, 57(7): 1354-1364.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.07.010    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I7/1354

图 1  GeNE模型的结构
数据集 N T Pt V
合成 50 000 10 20 3
地震 461 21 24 1
WebTraffic 142 753 12 30 1
INS 241 045 15 24 2
TMP 16 792 3 30 12
MCE 3 833 213 12 4 2
表 1  使用的6个数据集详细情况统计
指标 H Co
K-means 0.546 0.091
Agllo 0.533 0.089
Birch 0.537 0.092
HMM 0.612 0.101
GMM 0.637 0.112
GeNE 0.674 0.158
表 2  不同方法对合成数据的识别性能
数据集 MAPE
ARIMA LSTM TRMF CVAE GeNE
地震 0.343 0.314 0.222 0.258 0.221
WebTraffic 4.438 3.937 3.091 3.166 2.945
MCE 0.782 0.694 0.574 0.581 0.539
INS 3.654 3.247 2.935 2.797 2.751
TMP 4.715 4.501 3.977 3.981 3.742
表 3  不同方法在5个数据集的回归性能（MAPE）
%
数据集 方法 A 数据集 方法 A
地震 NN-ED 68.22 WebTraffic NN-ED 73.40
地震 NN-DTW 70.31 WebTraffic NN-DTW 74.03
地震 NN-CID 69.41 WebTraffic NN-CID 74.26
地震 FS 74.66 WebTraffic FS 73.89
地震 TSF 74.67 WebTraffic TSF 75.38
地震 SAX-VSM 73.76 WebTraffic SAX-VSM 74.91
地震 MC-DCNN 70.29 WebTraffic MC-DCNN 75.29
地震 LSTM 68.35 WebTraffic LSTM 73.15
地震 CVAE 74.82 WebTraffic CVAE 75.17
地震 GeNE 75.54 WebTraffic GeNE 75.91
表 4  采用不同方法对地震和WebTraffic数据集的分类性能
%
数据集 方法 P R F1 F0.5
MCE NN-ED 59.90 34.82 44.01 52.38
MCE NN-DTW 60.17 41.41 49.04 55.15
MCE NN-CID 57.12 40.86 47.55 52.93
MCE FS 54.34 43.54 48.34 51.74
MCE TSF 76.80 52.61 62.50 70.30
MCE SAX-VSM 65.12 59.96 62.44 64.01
MCE MC-DCNN 78.94 49.27 60.70 70.43
MCE LSTM 79.69 53.56 64.10 72.58
MCE CVAE 77.92 54.12 64.32 72.02
MCE GeNE 80.33 58.17 67.45 74.61
INS NN-ED 28.51 19.33 23.01 26.01
INS NN-DTW 27.14 21.73 24.13 25.84
INS NN-CID 52.65 10.25 17.05 28.75
INS FS 31.66 16.73 21.84 26.85
INS TSF 48.11 21.04 29.13 38.20
INS SAX-VSM 62.71 28.41 40.11 50.51
INS MC-DCNN 53.77 5.79 10.38 20.06
INS LSTM 60.25 28.01 38.23 48.93
INS CVAE 63.27 26.78 37.57 49.67
INS GeNE 71.50 33.15 45.34 58.01
TMP NN-ED 54.43 47.88 50.95 52.92
TMP NN-DTW 51.95 52.43 52.14 52.04
TMP NN-CID 56.12 49.26 52.44 54.61
TMP FS 65.17 58.82 61.85 63.76
TMP TSF 54.20 60.94 57.42 55.47
TMP SAX-VSM 72.22 59.05 64.94 69.10
TMP MC-DCNN 76.79 66.13 71.06 74.37
TMP LSTM 56.21 53.15 54.63 55.69
TMP CVAE 74.86 59.22 66.14 71.15
TMP GeNE 80.23 64.57 71.55 76.51
表 5  采用不同方法对MCE、INS、TMP数据集的分类性能
图 2  GeNE在国家电网提供的数据集上的真实应用
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