Multivariate Deep Causal Network for Time Series Forecasting in Interdependent Networks
Multivariate Deep Causal Network for Time Series Forecasting in Interdependent Networks
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DOI：
10.1109/cdc.2018.8619668
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发表时间：
2018-12
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通讯作者：
Lalitha Madhavi Konila Madhavi-Lalitha-Madhavi-Konila-Madhavi-65888514;Mostafa Gilanifar;Yuxun Zhou;E. Ozguven;R. Arghandeh
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文献类型：
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作者：
Lalitha Madhavi Konila Madhavi-Lalitha-Madhavi-Konila-Madhavi-65888514;Mostafa Gilanifar;Yuxun Zhou;E. Ozguven;R. Arghandeh
本文提出了一种新的多元深度因果网络模型（MDCN），该模型结合条件方差理论和深度神经网络来识别不同相互依赖时间序列之间的因果关系。MCDN验证通过双步法进行。自我验证是由基于信息论的指标来执行的，交叉验证是由一个森林应用程序来实现的，该应用程序结合了美国佛罗里达塔拉哈西市实际相互依赖的电力、交通和天气数据集。
A novel multivariate deep causal network model (MDCN) is proposed in this paper, which combines the theory of conditional variance and deep neural networks to identify the cause-effect relationship between different interdependent time-series. The MCDN validation is conducted by a double step approach. The self validation is performed by information theory - based metrics, and the cross validation is achieved by a foresting application that combines the actual interdependent electricity, transportation, and weather datasets in the City of Tallahassee, Florida, USA.