The parametric modified limited penetrable visibility graph for constructing complex networks from time series

Li, Xiuming; Sun, Mei; Gao, Cuixia; Han, Dun; Wang, Minggang

doi:10.1016/j.physa.2017.11.040

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…When there is a window slide moving in the time series, the time-dependent limited penetrable visibility graph (TDLPVG) algorithm has been developed, which can analyze the statistical properties of the time series [ 109 ]. The parametric modified limited penetrable visibility graph (PMLPVG) is then improved based on the LPVG [ 110 ], so that it shows the dynamic properties of time series and enhances the reasonability of the penetrable visibility, which has been tested in different real-world time series. Due to the unavoidability of noise in time series, the improved power of scale-freeness of VG (PSVG) is developed to measure the fractality of time series by analyzing the fractality in different scales [ 111 ].…”

Section: Visibility Graphmentioning

confidence: 99%

Visibility graph for time series prediction and image classification: a review

2022

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The analysis of time series and images is significant across different fields due to their widespread applications. In the past few decades, many approaches have been developed, including data-driven artificial intelligence methods, mechanism-driven physical methods, and hybrid mechanism and data-driven models. Complex networks have been used to model numerous complex systems due to its characteristics, including time series prediction and image classification. In order to map time series and images into complex networks, many visibility graph algorithms have been developed, such as horizontal visibility graph, limited penetrable visibility graph, multiplex visibility graph, and image visibility graph. The family of visibility graph algorithms will construct different types of complex networks, including (un-) weighted, (un-) directed, and (single-) multi-layered networks, thereby focusing on different kinds of properties. Different types of visibility graph algorithms will be reviewed in this paper. Through exploring the topological structure and information in the network based on statistical physics, the property of time series and images can be discovered. In order to forecast (multivariate) time series, several variations of local random walk algorithms and different information fusion approaches are applied to measure the similarity between nodes in the network. Different forecasting frameworks are also proposed to consider the information in the time series based on the similarity. In order to classify the image, several machine learning models (such as support vector machine and linear discriminant) are used to classify images based on global features, local features, and multiplex features. Through various simulations on a variety of datasets, researchers have found that the visibility graph algorithm outperformed existing algorithms, both in time series prediction and image classification. Clearly, complex networks are closely connected with time series and images by visibility graph algorithms, rendering complex networks to be an important tool for understanding the characteristics of time series and images. Finally, we conclude in the last section with future outlooks for the visibility graph.

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Section: Visibility Graphmentioning

confidence: 99%

Visibility graph for time series prediction and image classification: a review

2022

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“…The above deep learning methods, using single-channel information derived from raw polysomnographic recordings, fulfills the task of sleep stage detection. Additionally, the LPVG method has been applied to time series analysis from graph theory [24][25][26]. In this work, we combine the LPVG and CNN to detect the sleep stages from a single-channel EEG signal.…”

Section: Introductionmentioning

confidence: 99%

A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals

Cai

Gao

et al. 2021

IEEE Trans. Circuits Syst. II

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Sleep is an essential integrant in everyone's daily life. Thereby, it is an important but challenging problem to construct a reliable and stable system, that can monitor user's sleep quality automatically. In this work, we combine complex network and deep learning to propose a novel Graph-Temporal fused dual-input Convolutional Neural Network (CNN) method to detect sleep stages by using the Sleep-EDF database. Firstly, we segment each single-channel EEG signal into non-overlapping 30s epochs to set up the network. For that, we map each epoch into a Limited Penetrable Visibility Graph (LPVG) and obtain the corresponding Degree Sequence (DS) by calculating the node degree. Finally, the DSs and the 30s EEG epochs are combined as inputs of the novel Graph-Temporal fused dual-input CNN to learn about the graph topology and about the temporal feature representations of the raw data for the purpose of classifying the sleep stages into the two-, three-, four-, five-, and six-state. Notably, the classification accuracy of six-state stage detection is 87.21% and the corresponding Kappa value is 0.80. The results demonstrate the effectiveness of our model structure in detecting sleep states, whereby they further provide a basic strategy for future sleep research.

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“…Its advantage is that the associated network not only inherits the inherent characteristics of the original time series, but also is convenient to analyze the dynamic characteristics of the time series. Therefore, visibility graph, horizontal visibility graph, penetrable visibility graph, directed limited penetrable visibility graph have gradually become important branches in the study of time series [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Research on the Interdependent Network of the Thermal Coal Price Based on Directed Limited Penetrable Visibility Graph

Zhang,

Tian,

et al. 2021

Preprint

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This paper constructs a new directed limited penetrable interdependent network (DLPIN) for the thermal coal price (TCP) between the opening series and closing series based on the criterion of the visibility graph, which is better than the traditional method of the visibility graph to mine the price information for steam coal. According to the DLPIN by analyzing, the mechanism of the price fluctuation and information transmission for the thermal coal can be obtained, some references can be provided for the investors to reduce risk investment and increase revenue.

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The parametric modified limited penetrable visibility graph for constructing complex networks from time series

Cited by 15 publications

References 32 publications

Visibility graph for time series prediction and image classification: a review

Visibility graph for time series prediction and image classification: a review

A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals

Research on the Interdependent Network of the Thermal Coal Price Based on Directed Limited Penetrable Visibility Graph

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