Hurst Space Analysis, data clustering technique for long-range correlated time series
Abstract
It was shown for variables across different complex systems that their fluctuation functions calculated with detrending methods of scaling analysis are rarely, as in theory, ideal linear functions on log-log graphs of scale dependence. Instead, they frequently exhibit existence of transient crossovers in behavior, signs of trends that arise as effects of periodic or aperiodic cycles (Hu et al., 2001). The use of global and local wavelet transform spectral analysis (WTS) and their detrended fluctuation analysis (DFA) variants provides a possibility to detect these cyclic trends and to investigate their timing, nature and effects on the analyzed time series.
Source:
Conference on Complex Systems, 7-11 December 2020, 2020Funding / projects:
- Phase Transitions and Characterization of Inorganic and Organic Systems (RS-MESTD-Basic Research (BR or ON)-171015)
- Advanced analytical, numerical and analysis methods of applied fluid mechanics and complex systems (RS-MESTD-Basic Research (BR or ON)-174014)
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- Book of Abstracts
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Fakultet veterinarske medicineTY - CONF AU - Blesić, Suzana AU - Sarvan, Darko PY - 2020 UR - https://vet-erinar.vet.bg.ac.rs/handle/123456789/2752 AB - It was shown for variables across different complex systems that their fluctuation functions calculated with detrending methods of scaling analysis are rarely, as in theory, ideal linear functions on log-log graphs of scale dependence. Instead, they frequently exhibit existence of transient crossovers in behavior, signs of trends that arise as effects of periodic or aperiodic cycles (Hu et al., 2001). The use of global and local wavelet transform spectral analysis (WTS) and their detrended fluctuation analysis (DFA) variants provides a possibility to detect these cyclic trends and to investigate their timing, nature and effects on the analyzed time series. C3 - Conference on Complex Systems, 7-11 December 2020 T1 - Hurst Space Analysis, data clustering technique for long-range correlated time series UR - https://hdl.handle.net/21.15107/rcub_veterinar_2752 ER -
@conference{ author = "Blesić, Suzana and Sarvan, Darko", year = "2020", abstract = "It was shown for variables across different complex systems that their fluctuation functions calculated with detrending methods of scaling analysis are rarely, as in theory, ideal linear functions on log-log graphs of scale dependence. Instead, they frequently exhibit existence of transient crossovers in behavior, signs of trends that arise as effects of periodic or aperiodic cycles (Hu et al., 2001). The use of global and local wavelet transform spectral analysis (WTS) and their detrended fluctuation analysis (DFA) variants provides a possibility to detect these cyclic trends and to investigate their timing, nature and effects on the analyzed time series.", journal = "Conference on Complex Systems, 7-11 December 2020", title = "Hurst Space Analysis, data clustering technique for long-range correlated time series", url = "https://hdl.handle.net/21.15107/rcub_veterinar_2752" }
Blesić, S.,& Sarvan, D.. (2020). Hurst Space Analysis, data clustering technique for long-range correlated time series. in Conference on Complex Systems, 7-11 December 2020. https://hdl.handle.net/21.15107/rcub_veterinar_2752
Blesić S, Sarvan D. Hurst Space Analysis, data clustering technique for long-range correlated time series. in Conference on Complex Systems, 7-11 December 2020. 2020;. https://hdl.handle.net/21.15107/rcub_veterinar_2752 .
Blesić, Suzana, Sarvan, Darko, "Hurst Space Analysis, data clustering technique for long-range correlated time series" in Conference on Complex Systems, 7-11 December 2020 (2020), https://hdl.handle.net/21.15107/rcub_veterinar_2752 .