Ordinal pattern and statistical complexity analysis of daily stream flow time series

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

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Analysis of long-term fluctuations in stream flow time series: An application to Litani River, Lebanon

Acta Geophysica ◽

10.2478/s11600-013-0175-4 ◽

2013 ◽

Vol 62 (1) ◽

pp. 164-179 ◽

Cited By ~ 6

Author(s):

Amin Shaban ◽

Luciano Telesca ◽

Talal Darwich ◽

Nabil Amacha

Keyword(s):

Time Series ◽

Stream Flow ◽

Flow Time ◽

Litani River

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Research on Attention EEG Based on Jensen-Shannon Divergence

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.884-885.512 ◽

2014 ◽

Vol 884-885 ◽

pp. 512-515

Author(s):

Zheng Xia Zhang ◽

Si Qiu Xu ◽

Er Ning Zhou ◽

Xiao Lin Huang ◽

Jun Wang

Keyword(s):

Time Series ◽

Brain Function ◽

Complexity Analysis ◽

The State ◽

Function Evaluation ◽

Eeg Signals ◽

Alpha Wave ◽

Eyes Closed ◽

Statistical Complexity ◽

Jensen Shannon Divergence

The article adopted the Jensen - Shannon Divergence analysis method for alpha wave EEG complexity analysis, used to quantify the three different status (Eyes closed, count, idle) degree of coupling between acquisition of EEG time series. The algorithm are used to calculate the statistical complexity of alpha wave EEG signals then T test, the results show that the state of eyes closed and idle under the coupling degree between EEG time series, and the state of eyes closed and counting, counting and daze cases EEG time series have significant differences. Thus JSD algorithm can be used to analyze EEG signals attention, statistical complexity can be used as a measure of brain function parameters and would be applied to the auxiliary clinical brain function evaluation in the future.

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Multiscale Jensen-Shannon Divergence Based Analysis of Beta Wave Attention EEG

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.574.723 ◽

2014 ◽

Vol 574 ◽

pp. 723-727

Author(s):

Zheng Xia Zhang ◽

Si Qiu Xu ◽

Er Ning Zhou ◽

Xiao Lin Huang ◽

Jun Wang

Keyword(s):

Time Series ◽

Brain Function ◽

Complexity Analysis ◽

Function Parameter ◽

Function Evaluation ◽

Eeg Signals ◽

Multi Scale ◽

Eyes Closed ◽

Statistical Complexity ◽

Jensen Shannon Divergence

The article adopted the multiscale Jensen - Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states of Beta EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze),we are aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data, finding that there are large amounts of nonlinear time series in the Beta EEG signals. This method is also fully proved that the multi-scale JSD algorithm can be used to analyze EEG signals, attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

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Complexity analysis of the environmental fluid flow time series

Developments in Environmental Modelling - Time and Methods in Environmental Interfaces Modelling - Personal Insights ◽

10.1016/b978-0-444-63918-9.00017-x ◽

2017 ◽

pp. 207-234

Author(s):

D.T. Mihailović ◽

I. Balaž ◽

D. Kapor

Keyword(s):

Time Series ◽

Fluid Flow ◽

Complexity Analysis ◽

Flow Time ◽

Environmental Fluid

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Changes in land cover and stream flows in Gilgel Abbay catchment, Upper Blue Nile basin – Ethiopia

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-9567-2010 ◽

2010 ◽

Vol 7 (6) ◽

pp. 9567-9598 ◽

Cited By ~ 4

Author(s):

T. H. M. Rientjes ◽

A. T. Haile ◽

C. M. M. Mannaerts ◽

E. Kebede ◽

E. Habib

Keyword(s):

Time Series ◽

Land Cover ◽

Stream Flow ◽

Agricultural Land ◽

Flow Time ◽

Nile Basin ◽

Classification Analysis ◽

Blue Nile ◽

Blue Nile Basin ◽

Upper Blue Nile Basin

Abstract. We evaluated the land cover change in the Upper Gilgel Abbay catchment in the Upper Blue Nile basin through classification analysis of remote sensing based land cover data and through assessing the changes in the hydrological regime by statistical analysis of stream flow observations. Results of the land cover classification analysis indicated that 50.9% and 16.7% of the catchment area was covered by forest in 1973 and 2001, respectively. This significant decrease in forest cover is mainly due to expansion of agricultural land. A comparison of stream flow time series of the Upper Gilgel Abbay catchment to stream flow time series from two neighbouring catchments shows a different trend and a statistically significant change over time. In 1986–2001, the annual and the high flows of the catchment increased by 13% and 46%, respectively while the low flows decreased by 35%. Generally, the results indicate significant changes in land cover and the hydrological regimes of the Upper Gilgel Abbay catchment over the past 30 years.

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