Multiscale recurrence plot and recurrence quantification analysis for financial time series

Yin, Yawei; Shang, Pengjian

doi:10.1007/s11071-016-2830-1

Cited by 25 publications

(6 citation statements)

References 52 publications

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“…(b) Windowed scale-specific recurrence network analysis Scale-specific recurrence network analysis (SSRNA) provides an extension of traditional RNA where the input time series is filtered for variations at distinct time scales (or, equivalently, frequencies) before transforming it to a network as outlined above. Thereby, different time scales of the system can be studied separately, which has already been successfully applied to classify systems of various types [31][32][33][34][35][36]. We emphasize that in some cases, the term 'multi-scale' analysis has been used in the past to describe this type of analysis.…”

Section: Theorymentioning

confidence: 99%

Areawise significance tests for windowed recurrence network analysis

Lekscha

Donner

2019

Proc. R. Soc. A.

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Many time-series analysis techniques use sliding window approaches or are repeatedly applied over a continuous range of parameters. When combined with a significance test, intrinsic correlations among the pointwise analysis results can make falsely positive significant points appear as continuous patches rather than as isolated points. To account for this effect, we present an areawise significance test that identifies such false-positive patches. For this purpose, we numerically estimate the decorrelation length of the statistic of interest by calculating correlation functions between the analysis results and require an areawise significant point to belong to a patch of pointwise significant points that is larger than this decorrelation length. We apply our areawise test to results from windowed traditional and scale-specific recurrence network analysis in order to identify dynamical anomalies in time series of a non-stationary Rössler system and tree ring width index values from Eastern Canada. Especially, in the palaeoclimate context, the areawise testing approach markedly reduces the number of points that are identified as significant and therefore highlights only the most relevant features in the data. This provides a crucial step towards further establishing recurrence networks as a tool for palaeoclimate data analysis.

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Section: Theorymentioning

confidence: 99%

Areawise significance tests for windowed recurrence network analysis

Lekscha

Donner

2019

Proc. R. Soc. A.

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“…Recently, based on recurrence plots, an alternative approach has been developed: the Quadrant Scan. Although, the RP method has been widely used for the analysis of time series from fields ranging from finance to physics to biology [34,[44][45][46][47][48][49][50][51], Quadrant Scan is a relatively new method developed to detect transitions in system dynamics. It has been successfully applied to time series as well as non-temporal signals.…”

Section: Introductionmentioning

confidence: 99%

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

Zaitouny

Fragkou

Stemler

et al. 2022

Sensors

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Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

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“…At the same time, the recurrence plot [ 18 , 19 , 20 ] and its recurrence quantification analysis (RQA) [ 21 , 22 , 23 ], which are related to the embedding dimension, have been successfully applied in analyzing the dynamic characteristics of complex systems, and have been applied to other fields such as economics and sociology [ 24 , 25 , 26 , 27 , 28 ]. What is more, the measures of RQA have been proven to be correlated with the embedding dimension by Zbilut [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Determining the Embedding Dimension of Financial Time Series Based on Manhattan Distance and Recurrence Quantification Analysis

Zhu

Huang

2022

Entropy

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Identification of embedding dimension is helpful to the reconstruction of phase space. However, it is difficult to calculate the proper embedding dimension for the financial time series of dynamics. By this Letter, we suggest a new method based on Manhattan distance and recurrence quantification analysis for determining the embedding dimension. By the advantages of the above two tools, the new method can calculate the proper embedding dimension with the feature of stability, accuracy and rigor. Besides, it also has a good performance on the chaotic time series which has a high-dimensional attractors.

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Multiscale recurrence plot and recurrence quantification analysis for financial time series

Cited by 25 publications

References 52 publications

Areawise significance tests for windowed recurrence network analysis

Areawise significance tests for windowed recurrence network analysis

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

A New Method for Determining the Embedding Dimension of Financial Time Series Based on Manhattan Distance and Recurrence Quantification Analysis

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