Causal Identification Based on Compressive Sensing of Air Pollutants Using Urban Big Data

Li, Mingwei; Li, Jinpeng; Wan, Shuangning; Chen, Hao; Liu, Chao

doi:10.1109/access.2020.3000767

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Granger causality and its extensions, along with the alternative causality measures that have been developed afterwards, are vastly used in different applications. Among others, causality measures are utilized in financial applications, e.g., for the examination of the relation of stock markets [ 175 , 176 ], in neuroscience, e.g., for the analysis of brain structures and physiological time series [ 150 , 169 , 177 ], in seismology, e.g., for the analysis of earthquake data [ 178 ], in geoscience, e.g., for the discovery of weather and vegetation conditions on global wildfire [ 179 ], in meteorology, e.g., for modeling the air quality [ 180 , 181 ], and in epidemiology [ 182 , 183 ].…”

Section: Directional Connectivity Measuresmentioning

confidence: 99%

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Papana

2021

Entropy

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The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

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Section: Directional Connectivity Measuresmentioning

confidence: 99%

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Papana

2021

Entropy

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DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

et al. 2022

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The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information.

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Causal Identification Based on Compressive Sensing of Air Pollutants Using Urban Big Data

Cited by 2 publications

References 25 publications

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing

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