An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Zhao, Junyin; Mo, Hongming; Deng, Yong

doi:10.1109/access.2020.2964067

Cited by 18 publications

(8 citation statements)

References 62 publications

(71 reference statements)

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“…More graph-theoretical techniques including horizontal visibility graph [51], multiplex visibility graph [52], and recurrence networks [11] would give new criteria for categorizing or unifying different seismic activities. A novel approach for forecasting time series based on visibility graph [53] might find potential application for earthquakes. Our study therefore shows with affirmation that the visibility graph algorithm has the potentiality to capture the non-trivial complexity inherent in a time series which is non-linear and non-stationary in nature.…”

Section: Discussionmentioning

confidence: 99%

Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

et al. 2021

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Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.

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Section: Discussionmentioning

confidence: 99%

Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

et al. 2021

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“…In this application, a more specific and practical case is offered to examine correctness and validity of TDQMF in ability of predictions based on actual conditions, namely income estimate. In fact, many researches on prediction about future trends which are even in the form of quantum have been made [27,39,[53][54][55]. Focused on the trend of changes of income, TDQMF is utilized to predict it based on provided effective and certain quantum evidences so as to give comparatively reasonable estimates.…”

Section: Application Of Income Estimatementioning

confidence: 99%

TDQMF: Two-dimensional quantum mass function

He¹

2021

Preprint

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Quantum mass function has been applied in lots of fields because of its efficiency and validity of managing uncertainties in the form of quantum which can be regarded as an extension of classical Dempster-Shafer (D-S) evidence theory. However, how to handle uncertainties in the form of quantum is still an open issue. In this paper, a new method is proposed to dispose uncertain quantum information, which is called two-dimensional quantum mass function (TDQMF). A TDQMF is consist of two elements, TQ = (Q original , Q indicative ), both of the Qs are quantum mass functions, in which the Q indicative is an indicator of the reliability on Q original . More flexibility and effectiveness are offered in handling uncertainty in the field of quantum by the proposed method compared with primary quantum mass function. Besides, some numerical examples are provided and some practical applications are given to verify its correctness and validity.

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“…The information considered in prediction models includes the information in the last data point ( ) [ 74 ], the relationship between and its most similar node(s) [ 79 ], and the adjacent information [ 80 ]. The weight parameters of the prediction model can also be determined by the distance between data points [ 79 , 81 ]. The induced-ordered weighted averaging aggregation (IOWA) operation and network-based multiple time-frequency spaces were applied to forecast time series to improve the accuracy [ 82 ].…”

Section: Introductionmentioning

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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An Efficient Network Method for Time Series Forecasting Based on the DC Algorithm and Visibility Relation

Cited by 18 publications

References 62 publications

Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

TDQMF: Two-dimensional quantum mass function

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

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