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Acta Physica Sinica, Volume. 68, Issue 23, 238901-1(2019)

A symbolized time series network based on seasonal-trend-loess method

Li-Na Wang1,2、*, Yuan-Yuan Cheng1, and Chen-Rui Zang3
Author Affiliations
  • 1College of Sciences, Inner Mongolian University of Technology, Hohhot 010051, China
  • 2Inner Mongolian Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot 010051, China
  • 3Inner Mongolian Branch, China Unicom, Hohhot 010050, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
    Equations (7)
    References (27)
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    Figures & Tables(8)
    (a)−(d) The STL analyzing for the air passengers throughput time series: (a) Original time series; (b) seasonal time series; (c) trend time series; (d) remainder time series; (e) the time series network of the air passengers throughput data.(a)−(d)航空旅客吞吐量时间序列的STL分析 (a)原始时间序列; (b)季节项时间序列; (c) 趋势项时间序列; (d) 随机项时间序列; (e)航空旅客吞吐量时间序列网络

    Fig. 1. (a)−(d) The STL analyzing for the air passengers throughput time series: (a) Original time series; (b) seasonal time series; (c) trend time series; (d) remainder time series; (e) the time series network of the air passengers throughput data.(a)−(d)航空旅客吞吐量时间序列的STL分析　(a)原始时间序列; (b)季节项时间序列; (c) 趋势项时间序列; (d) 随机项时间序列; (e)航空旅客吞吐量时间序列网络

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    The degree distribution of the time series network for air passengers throughput data: (a) The cumulative weighted in-degree distribution; (b) the cumulative weighted out-degree distribution; (c) the cumulative weighted degree distribution.航空旅客吞吐量时间序列网络度分布 (a)累积加权入度分布; (b)累积加权出度分布; (c)累积加权度分布

    Fig. 2. The degree distribution of the time series network for air passengers throughput data: (a) The cumulative weighted in-degree distribution; (b) the cumulative weighted out-degree distribution; (c) the cumulative weighted degree distribution.航空旅客吞吐量时间序列网络度分布　(a)累积加权入度分布; (b)累积加权出度分布; (c)累积加权度分布

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    (a)−(d) The STL decomposition results of the Internet traffic time series: (a) Original time series; (b) seasonal time series; (c) trend time series; (d) remainder time series; (e) the time series network of the Internet traffic data.(a)−(d)因特网流量时间序列的STL分析 (a)原始时间序列; (b)季节项时间序列; (c) 趋势项时间序列; (d) 随机项时间序列; (e)因特网流量时间序列网络

    Fig. 3. (a)−(d) The STL decomposition results of the Internet traffic time series: (a) Original time series; (b) seasonal time series; (c) trend time series; (d) remainder time series; (e) the time series network of the Internet traffic data.(a)−(d)因特网流量时间序列的STL分析　(a)原始时间序列; (b)季节项时间序列; (c) 趋势项时间序列; (d) 随机项时间序列; (e)因特网流量时间序列网络

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    The degree distribution of the time series network for the Internet traffic data: (a) The cumulative weighted in-degree distribution; (b) the cumulative weighted out-degree distribution; (c) the cumulative weighted degree distribution.因特网流量时间序列网络的度分布 (a)累积加权入度分布; (b)累积加权出度分布; (c)累积加权度分布

    Fig. 4. The degree distribution of the time series network for the Internet traffic data: (a) The cumulative weighted in-degree distribution; (b) the cumulative weighted out-degree distribution; (c) the cumulative weighted degree distribution.因特网流量时间序列网络的度分布　(a)累积加权入度分布; (b)累积加权出度分布; (c)累积加权度分布

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    (a)−(d) The STL analyzing for the mobile traffic data: (a) Original time series; (b) seasonal time series; (c) trend time series; (d) remainder time series; (e) based on the STL decomposition, the time series network of the mobile traffic data.(a)−(d)语音时间序列数据的STL分析 (a)原始时间序列; (b)季节项时间序列; (c) 趋势项时间序列; (d) 随机项时间序列; (e)基于STL方法的语音时间序列网络

    Fig. 5. (a)−(d) The STL analyzing for the mobile traffic data: (a) Original time series; (b) seasonal time series; (c) trend time series; (d) remainder time series; (e) based on the STL decomposition, the time series network of the mobile traffic data.(a)−(d)语音时间序列数据的STL分析　(a)原始时间序列; (b)季节项时间序列; (c) 趋势项时间序列; (d) 随机项时间序列; (e)基于STL方法的语音时间序列网络

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    The degree distribution of the time series network for the mobile traffic data: (a) The cumulative weighted in-degree distribution; (b) the cumulative weighted out-degree distribution; (c) the cumulative weighted degree distribution.语音时间序列网络的度分布 (a)累积加权入度分布; (b)累积加权出度分布; (c)累积加权度分布

    Fig. 6. The degree distribution of the time series network for the mobile traffic data: (a) The cumulative weighted in-degree distribution; (b) the cumulative weighted out-degree distribution; (c) the cumulative weighted degree distribution.语音时间序列网络的度分布　(a)累积加权入度分布; (b)累积加权出度分布; (c)累积加权度分布

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    • Table 1.

      The comparison for topological characteristics of two kinds time series networks.

      两类时间序列网络拓扑特征的比较

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      Table 1.

      The comparison for topological characteristics of two kinds time series networks.

      两类时间序列网络拓扑特征的比较

      时间序列网络拓扑特征
      长度周期节点数平均加权度聚类系数平均路径长度加权度分布
      航空旅客吞吐量264121074.4300.16913.355指数分布
      因特网流量31682881605.5380.24925.610幂律分布

    • Table 2.

      The table for characteristics of node patterns.

      网络节点模式特征表

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      Table 2.

      The table for characteristics of node patterns.

      网络节点模式特征表

      节点聚类系数节点加权出度节点介数
      dcb1faa3874eoa9810.72
      daa1aaa3780hia9605.21
      aac1haa2597faa9295.21
      deb1eaa2570eaa8532.04
      dfb1gaa2564haa6180.21
      dgb1daa1279aba4185.66
      egc1aba890ana3933.32
      aqb1fba765aoa3649.48
      aob1eba564fra3475.81
      dkb1hba550aga3389.27
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    Li-Na Wang, Yuan-Yuan Cheng, Chen-Rui Zang. A symbolized time series network based on seasonal-trend-loess method[J]. Acta Physica Sinica, 2019, 68(23): 238901-1

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    Paper Information

    Received: May. 24, 2019

    Accepted: --

    Published Online: Sep. 17, 2020

    The Author Email:

    DOI:10.7498/aps.68.20190794

    Topics

    Nuclear physics
    Particles and fields
    Particle and nuclear astrophysics and cosmology