This paper proposes a novel causality analysis approach called the causal Markov Elman network (CMEN) to characterize the interdependence among heterogeneous time series in multinetwork systems. The CMEN performance, which comprises inputs filtered by Markov property, successfully characterizes various multivariate dependencies in an urban environment. This paper also proposes a novel hypothesis of characterizing joint information between interconnected systems such as electricity and transportation networks. The proposed methodology and the hypotheses are then validated by information theory distance-based metrics. For cross validation, the CMEN is applied to the electricity load forecasting problem using actual data from Tallahassee, Florida.

中文翻译：

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用于多网络系统中负载预测的因果马尔可夫埃尔曼网络


本文提出了一种称为因果马尔可夫埃尔曼网络（CMEN）的新型因果分析方法，用于表征多网络系统中异构时间序列之间的相互依赖性。 CMEN 性能由马尔可夫属性过滤的输入组成，成功地表征了城市环境中的各种多元依赖性。本文还提出了一种表征电力和交通网络等互连系统之间联合信息的新假设。然后通过信息论基于距离的度量来验证所提出的方法和假设。为了进行交叉验证，使用佛罗里达州塔拉哈西的实际数据将 CMEN 应用于电力负荷预测问题。