A comparison of two methods for modeling large-scale data from time series as complex networks

Liu, Ying; Caö, Hongduo; Tan, Yong

doi:10.1063/1.3556121

Cited by 13 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This advantage makes the proposed method a potential candidate for real-world applications with low levels of noise background. In a recent work, Li et al reported that a judgment distance, defined as rate of maximum state transition distance to total number of nodes in a multidimensional phase space of a time series signal, can distinguish a chaotic series from a random series for a signal-to-noise ratio (SNR) over 50 (SNR ≥ 50) [16].…”

Section: A Discussion On Results Obtained For Biological Dynamical Symentioning

confidence: 99%

A distance-based dynamical transition analysis of time series signals and application to biological systems

Alagoz

Alagöz

2011

J Biol Phys

View full text Add to dashboard Cite

This study demonstrates an application of distance-based numerical measures to the phase space of time series signals, in order to obtain a temporal analysis of complex dynamical systems. This method is capable of detecting alterations appearing in the characters of the deterministic dynamical systems and provides a simple tool for the real-time analysis of time series data obtained from a complex dynamical system even with black box functionality. The study presents a possible application of the method in the dynamical transition analysis of real EEG records from epilepsy patients.

show abstract

Section: A Discussion On Results Obtained For Biological Dynamical Symentioning

confidence: 99%

A distance-based dynamical transition analysis of time series signals and application to biological systems

Alagoz

Alagöz

2011

J Biol Phys

View full text Add to dashboard Cite

show abstract

“…Primary among these methods is correlation coefficient. We provide a detailed comparison of the methods that are based on space distance, and that are based on correlation coefficient in principle, effect, and application scope [26]. Different connecting methods will result in different graphs.…”

Section: Related Referencesmentioning

confidence: 99%

Novel method of identifying time series based on network graphs

Caö

Tan

2011

Complexity

Self Cite

View full text Add to dashboard Cite

In this article, we propose a novel method for transforming a time series into a complex network graph. The proposed algorithm is based on the spatial distribution of a time series. The characteristics of geometric parameters of a network represent the dynamic characteristics of a time series. Our algorithm transforms, respectively, a constant series into a fully connected graph, periodic time series into a regular graph, linear divergent time series into a tree, and chaotic time series into an approximately power law distribution network graph. We find that when the dimension of reconstructed phase space increases, the corresponding graph for a random time series quickly turns into a completely unconnected graph, while that for a chaotic time series maintains a certain level of connectivity. The characteristics of the generated network, including the total edges, the degree distribution, and the clustering coefficient, reflect the characteristics of the time series, including diverging speed, level of certainty, and level of randomness. This observation allows a chaotic time series to be easily identified from a random time series. The method may be useful for analysis of complex nonlinear systems such as chaos and random systems, by perceiving the differences in the outcomes of the systems-the time series-in the identification of the systemic levels of certainty or randomness.

show abstract

“…Using complex network theory to study stock prices not only allows us to analyze the relationship between different stocks, but also allows us to explore the macroaspects of the comovement characteristics of the market in different periods [9][10][11]. Previous studies have proposed a variety of methods to build complex networks using the time series of stock prices, including visibility graphs [12][13][14], recurrence networks [15][16][17], correlation networks [11,18,19], pattern networks [10,20], and K-neighbors networks [21,22]. Of all the network construction methods, the symbolic pattern network is favored by many scholars because it can more accurately reflect the degree of correlation and direction of the primitive elements in a complex system [10,20,23,24].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Caö

Lin

et al. 2019

Complexity

Self Cite

View full text Add to dashboard Cite

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock’s price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. Firstly, we propose a new pattern network construction method for multivariate stock time series. The price volatility combination patterns of the Standard & Poor’s 500 Index (S&P 500), the NASDAQ Composite Index (NASDAQ), and the Dow Jones Industrial Average (DJIA) are transformed into directed weighted networks. It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market. Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K-nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next-day volatility patterns of a single stock. The results show that the optimal models corresponding to the two algorithms can be found through cross-validation and search methods, respectively. The prediction accuracy rates for the three indexes in relation to the testing data set are greater than 70%. In general, the prediction ability of SVM algorithms is better than that of KNN algorithms.

show abstract

A comparison of two methods for modeling large-scale data from time series as complex networks

Cited by 13 publications

References 14 publications

A distance-based dynamical transition analysis of time series signals and application to biological systems

A distance-based dynamical transition analysis of time series signals and application to biological systems

Novel method of identifying time series based on network graphs

Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Contact Info

Product

Resources

About