Hurst Space Analysis, data clustering technique for long-range correlated time series
Само за регистроване кориснике
2020
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Извор:
Conference on Complex Systems, 7-11 December 2020, 2020Финансирање / пројекти:
- Фазни прелази и карактеризација неорганских и органских система (RS-MESTD-Basic Research (BR or ON)-171015)
- Напредне аналитичке, нумеричке и методе анализе примењене механике флуида и комплексних система (RS-MESTD-Basic Research (BR or ON)-174014)
Напомена:
- Book of Abstracts
Колекције
Институција/група
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 .