 Generalized distance component method based on spatial amplitude and trend difference weighting operator for complex time seriesZhuo Wang and Pengjian Shang Chaos, Solitons & Fractals, 2023, vol. 176, issue C Abstract: The generalized distance component (GDISCO) approach, which uses the property that the energy distance is rotationally invariant in high-dimensional space to measure the complexity of univariate or multivariate time series, was recently proposed. However, because this method disregards the time series’ spatial trend distribution and amplitude structure, this work offers a new spatial amplitude and trend difference weighting operator, abbreviated as WSATD. We present the weighted generalized distance component (WSATD-GDISCO) technique based on WSATD. Numerical simulations show that WSATD-GDISCO is effective for short and long time series, and it is robust to noise. Compared to the GDISCO method, the WSATD-GDISCO shows better performance to measure the dynamic characteristics of a system. Finally, the method is applied to investigate the relationship between EEG and alcoholics’ response to stimuli, as well as the long-term and short-term differences in stock market volatility in China and the United States. Keywords: Complex time series; Weight; Distance components; Energy distance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010238 DOI: 10.1016/j.chaos.2023.114122 Access Statistics for this article Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros More articles in Chaos, Solitons & Fractals from Elsevier |
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