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Multi-fractal detre...
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de Melo Barros Junior, Paulo RobertoPetroleo Brasileiro SA, Brazil
(författare)
Multi-fractal detrended cross-correlation heatmaps for time series analysis
- Artikel/kapitelEngelska2022
Förlag, utgivningsår, omfång ...
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2022-12-15
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NATURE PORTFOLIO,2022
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electronicrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:liu-196969
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https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-196969URI
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https://doi.org/10.1038/s41598-022-26207-wDOI
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Språk:engelska
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Sammanfattning på:engelska
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Klassifikation
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:art swepub-publicationtype
Anmärkningar
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Funding Agencies|Swedish National Infrastructure for Computing (SNIC) [SNIC 2022/22-843]
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Complex systems in biology, climatology, medicine, and economy hold emergent properties such as non-linearity, adaptation, and self-organization. These emergent attributes can derive from large-scale relationships, connections, and interactive behavior despite not being apparent from their isolated components. It is possible to better comprehend complex systems by analyzing cross-correlations between time series. However, the accumulation of non-linear processes induces multiscale structures, therefore, a spectrum of power-law exponents (the fractal dimension) and distinct cyclical patterns. We propose the Multifractal detrended cross-correlation heatmaps (MF-DCCHM) based on the DCCA cross-correlation coefficients with sliding boxes, a systematic approach capable of mapping the relationships between fluctuations of signals on different scales and regimes. The MF-DCCHM uses the integrated series of magnitudes, sliding boxes with sizes of up to 5% of the entire series, and an average of DCCA coefficients on top of the heatmaps for the local analysis. The heatmaps have shown the same cyclical frequencies from the spectral analysis across different multifractal regimes. Our dataset is composed of sales and inventory from the Brazilian automotive sector and macroeconomic descriptors, namely the Gross Domestic Product (GDP) per capita, Nominal Exchange Rate (NER), and the Nominal Interest Rate (NIR) from the Central Bank of Brazil. Our results indicate cross-correlated patterns that can be directly compared with the power-law spectra for multiple regimes. We have also identified cyclical patterns of high intensities that coincide with the Brazilian presidential elections. The MF-DCCHM uncovers non-explicit cyclic patterns, quantifies the relations of two non-stationary signals (noise effect removed), and has outstanding potential for mapping cross-regime patterns in multiple domains.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Bunge, Kianny LopesUniFAMEC, Brazil
(författare)
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Serravalle Reis Rodrigues, Vitor HugoGeol Survey Brazil CPRM, Brazil
(författare)
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Ferreira Santiago, Michell ThompsonUniv Fed Bahia, Brazil; Univ Fed Bahia, Brazil
(författare)
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dos Santos Marinho, Euler BentesUniv Fed Bahia, Brazil; Univ Fed Bahia, Brazil
(författare)
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Lima de Jesus Silva, Jose LuisLinköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten(Swepub:liu)josli76
(författare)
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Petroleo Brasileiro SA, BrazilUniFAMEC, Brazil
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Scientific Reports: NATURE PORTFOLIO12:12045-2322
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