Complex Network Analysis of Recurrences

Donner, Reik V.; Donges, Jonathan F.; Zou, Yong; Feldhoff, Jan H.

doi:10.1007/978-3-319-07155-8_4

Cited by 9 publications

(6 citation statements)

References 105 publications

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“…It is expected that joint recurrence will be increasingly unlikely with an increase in the number m of processes. Therefore, Donner, Donges, Zou, and Feldhoff (2015) proposed a version called f ‐joint recurrence networks that reduces the requirement of occurrence of simultaneous recurrences in all subsystems.…”

Section: Mapping Multivariate Time Series Into Complex Networkmentioning

confidence: 99%

Time series analysis via network science: Concepts and algorithms

Silva

Ribeiro

et al. 2021

WIREs Data Min & Knowl

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There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space, or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining, and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics, and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified way and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition, and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation

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Section: Mapping Multivariate Time Series Into Complex Networkmentioning

confidence: 99%

Time series analysis via network science: Concepts and algorithms

Silva

Ribeiro

et al. 2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…The method of choice, recurrence network (RN) analysis of time series, is particularly useful for detecting qualitative changes in the dynamics of complex systems (Marwan et al, 2009;Donner et al, 2010b) and has been successfully applied in fields ranging from fluid dynamics to electrochemistry to physiology (Donner et al, 2014). RN analysis is specifically suitable for studying palaeoclimate records -unlike other methods there are only implicit effects of non-uniform sampling in the Table 1.…”

Section: Introductionmentioning

confidence: 99%

Non-linear regime shifts in Holocene Asian monsoon variability: potential impacts on cultural change and migratory patterns

et al. 2015

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Abstract. The Asian monsoon system is an important tipping element in Earth's climate with a large impact on human societies in the past and present. In light of the potentially severe impacts of present and future anthropogenic climate change on Asian hydrology, it is vital to understand the forcing mechanisms of past climatic regime shifts in the Asian monsoon domain. Here we use novel recurrence network analysis techniques for detecting episodes with pronounced non-linear changes in Holocene Asian monsoon dynamics recorded in speleothems from caves distributed throughout the major branches of the Asian monsoon system. A newly developed multi-proxy methodology explicitly considers dating uncertainties with the COPRA (COnstructing Proxy Records from Age models) approach and allows for detection of continental-scale regime shifts in the complexity of monsoon dynamics. Several epochs are characterised by non-linear regime shifts in Asian monsoon variability, including the periods around 8.5-7.9, 5.7-5.0, 4.1-3.7, and 3.0-2.4 ka BP. The timing of these regime shifts is consistent with known episodes of Holocene rapid climate change (RCC) and high-latitude Bond events. Additionally, we observe a previously rarely reported non-linear regime shift around 7.3 ka BP, a timing that matches the typical 1.0-1.5 ky return intervals of Bond events. A detailed review of previously suggested links between Holocene climatic changes in the Asian monsoon domain and the archaeological record indicates that, in addition to previously considered longer-term changes in mean monsoon intensity and other climatic parameters, regime shifts in monsoon complexity might have played an important role as drivers of migration, pronounced cultural changes, and the collapse of ancient human societies.

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“…8s points to each side. The window size of the wRNA is chosen according to the support and the minimum required window width of W = 100 for reliable results [13,16] as…”

Section: Theorymentioning

confidence: 99%

“…systems for which the dynamics cannot be assessed analytically. In particular, recurrence network analysis (RNA) [9][10][11][12][13][14] has been shown to be related to a generalized notion of dimensionality via the network transitivity and, thus, can be used to classify the complexity of the system's dynamics [15]. Locating possibly interesting effects in a non-stationary time series in time can be achieved by using a sliding window approach, that is, by splitting the time series into several possibly overlapping pieces and performing the analysis for each 'window' separately.…”

Section: Introductionmentioning

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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Complex Network Analysis of Recurrences

Cited by 9 publications

References 105 publications

Time series analysis via network science: Concepts and algorithms

Time series analysis via network science: Concepts and algorithms

Non-linear regime shifts in Holocene Asian monsoon variability: potential impacts on cultural change and migratory patterns

Areawise significance tests for windowed recurrence network analysis

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