scholarly journals Long-range temporal correlations in neural narrowband time-series arise due to critical dynamics

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0175628 ◽  
Author(s):  
Duncan A. J. Blythe ◽  
Vadim V. Nikulin
Keyword(s):  
Time Series ◽  
Long Range ◽  
Critical Dynamics ◽  
Temporal Correlations

scholarly journals Countrywide Monitoring of Ground Deformation Using InSAR Time Series: A Case Study from Qatar

2021 ◽  
Vol 13 (4) ◽  
pp. 702
Author(s):  
Mustafa Kemal Emil ◽  
Mohamed Sultan ◽  
Khaled Alakhras ◽  
Guzalay Sataer ◽  
Sabreen Gozi ◽  
...  
Keyword(s):  
Time Series ◽  
Arabian Peninsula ◽  
Ground Deformation ◽  
Anthropogenic Activities ◽  
Urban Centers ◽  
Alos Palsar ◽  
Groundwater Levels ◽  
Temporal Correlations ◽  
Time Series Approach ◽  
Sbas Insar

Over the past few decades the country of Qatar has been one of the fastest growing economies in the Middle East; it has witnessed a rapid increase in its population, growth of its urban centers, and development of its natural resources. These anthropogenic activities compounded with natural forcings (e.g., climate change) will most likely introduce environmental effects that should be assessed. In this manuscript, we identify and assess one of these effects, namely, ground deformation over the entire country of Qatar. We use the Small Baseline Subset (SBAS) InSAR time series approach in conjunction with ALOS Palsar-1 (January 2007 to March 2011) and Sentinel-1 (March 2017 to December 2019) synthetic aperture radar (SAR) datasets to assess ground deformation and conduct spatial and temporal correlations between the observed deformation with relevant datasets to identify the controlling factors. The findings indicate: (1) the deformation products revealed areas of subsidence and uplift with high vertical velocities of up to 35 mm/yr; (2) the deformation rates were consistent with those extracted from the continuously operating reference GPS stations of Qatar; (3) many inland and coastal sabkhas (salt flats) showed evidence for uplift (up to 35 mm/yr) due to the continuous evaporation of the saline waters within the sabkhas and the deposition of the evaporites in the surficial and near-surficial sabkha sediments; (4) the increased precipitation during Sentinel-1 period compared to the ALOS Palsar-1 period led to a rise in groundwater levels and an increase in the areas occupied by surface water within the sabkhas, which in turn increased the rate of deposition of the evaporitic sediments; (5) high subsidence rates (up to 14 mm/yr) were detected over landfills and dumpsites, caused by mechanical compaction and biochemical processes; and (6) the deformation rates over areas surrounding known sinkhole locations were low (+/−2 mm/yr). We suggest that this study can pave the way to similar countrywide studies over the remaining Arabian Peninsula countries and to the development of a ground motion monitoring system for the entire Arabian Peninsula.

scholarly journals On nonparametric regression for bivariate circular long-memory time series

2021 ◽  
Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh
Keyword(s):  
Time Series ◽  
Nonparametric Regression ◽  
Long Range ◽  
Long Memory ◽  
Regression Function ◽  
Confidence Bands ◽  
Long Range Dependence ◽  
Asymptotic Results ◽  
Memory Time ◽  
Circular Time Series

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

scholarly journals Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

2015 ◽  
Vol 6 ◽  
Author(s):  
Maria Botcharova ◽  
Luc Berthouze ◽  
Matthew J. Brookes ◽  
Gareth R. Barnes ◽  
Simon F. Farmer
Keyword(s):  
Long Range ◽  
Resting State ◽  
Finger Movement ◽  
Phase Synchrony ◽  
Temporal Correlations

scholarly journals Negative mood and mind wandering increase long-range temporal correlations in attention fluctuations

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196907 ◽  
Author(s):  
Mona Irrmischer ◽  
C. Natalie van der Wal ◽  
Huibert D. Mansvelder ◽  
Klaus Linkenkaer-Hansen
Keyword(s):  
Long Range ◽  
Negative Mood ◽  
Mind Wandering ◽  
Temporal Correlations

scholarly journals Strong long-range temporal correlations of beta/gamma oscillations are associated with poor sustained visual attention performance

2017 ◽  
Vol 48 (8) ◽  
pp. 2674-2683 ◽  
Author(s):  
Mona Irrmischer ◽  
Simon-Shlomo Poil ◽  
Huibert D. Mansvelder ◽  
Francesca Sangiuliano Intra ◽  
Klaus Linkenkaer-Hansen
Keyword(s):  
Visual Attention ◽  
Long Range ◽  
Gamma Oscillations ◽  
Temporal Correlations ◽  
Attention Performance

scholarly journals Architecture of Artificial Neural Network in Identification of Internal Dynamics and Prediction of Dynamic System Rainfall Data Time Series

2012 ◽  
pp. 34-42
Author(s):  
Sanjeev Karmakar ◽  
Manoj Kumar Kowar ◽  
Pulak Guhathakurta
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Dynamic System ◽  
Long Range ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Global Minima ◽  
Internal Dynamics ◽  
Artificial Neural

The objective of this study is to expand and evaluate the back-propagation artificial neural network (BPANN) and to apply in the identification of internal dynamics of very high dynamic system such long-range total rainfall data time series. This objective is considered via comprehensive review of literature (1978-2011). It is found that, detail of discussion concerning the architecture of ANN for the same is rarely visible in the literature; however various applications of ANN are available. The detail architecture of BPANN with its parameters, i.e., learning rate, number of hidden layers, number of neurons in hidden layers, number of input vectors in input layer, initial and optimized weights etc., designed learning algorithm, observations of local and global minima, and results have been discussed. It is observed that obtaining global minima is almost complicated and always a temporal nervousness. However, achievement of global minima for the period of the training has been discussed. It is found that, the application of the BPANN on identification for internal dynamics and prediction for the long-range total annual rainfall has produced good results. The results are explained through the strong association between rainfall predictors i.e., climate parameter (independent parameter) and total annual rainfall (dependent parameter) are presented in this paper as well.

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