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

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

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

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

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

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