SPATIO-TEMPORAL CORRELATIONS FROM fMRI TIME SERIES BASED ON THE NN-ARx MODEL

2010 ◽  
Vol 09 (04) ◽  
pp. 381-406 ◽  
Author(s):  
J. BOSCH-BAYARD ◽  
J. RIERA-DIAZ ◽  
R. BISCAY-LIRIO ◽  
K. F. K. WONG ◽  
A. GALKA ◽  
...  
Keyword(s):  
Time Series ◽  
Arx Model ◽  
Temporal Correlations ◽  
Fmri Time Series ◽  
Spatio Temporal

157. Spatio-temporal correlations in fMRI time series: The innovation approach

2008 ◽  
Vol 119 (9) ◽  
pp. e137-e138
Author(s):  
J. Bosch Bayard ◽  
J. Riera Díaz ◽  
R. Biscay Lirio ◽  
K. Wong ◽  
O. Yamashita ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Correlations ◽  
Fmri Time Series ◽  
Spatio Temporal ◽  
Innovation Approach

scholarly journals Towards Reliable Velocities of Permanent GNSS Stations

2016 ◽  
Vol 100 (1) ◽  
pp. 17-26
Author(s):  
Janusz Bogusz ◽  
Anna Klos ◽  
Marta Gruszczynska ◽  
Maciej Gruszczynski
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
North East ◽  
Temporal Correlations ◽  
The North ◽  
Spatio Temporal ◽  
Permanent Station

Abstract In the modern geodesy the role of the permanent station is growing constantly. The proper treatment of the time series from such station lead to the determination of the reliable velocities. In this paper we focused on some pre-analysis as well as analysis issues, which have to be performed upon the time series of the North, East and Up components and showed the best, in our opinion, methods of determination of periodicities (by means of Singular Spectrum Analysis) and spatio-temporal correlations (Principal Component Analysis), that still exist in the time series despite modelling. Finally, the velocities of the selected European permanent stations with the associated errors determined following power-law assumption in the stochastic part is presented.

scholarly journals Accurate modeling of temporal correlations in rapidly sampled fMRI time series

2018 ◽  
Vol 39 (10) ◽  
pp. 3884-3897 ◽  
Author(s):  
Nadège Corbin ◽  
Nick Todd ◽  
Karl J. Friston ◽  
Martina F. Callaghan
Keyword(s):  
Time Series ◽  
Temporal Correlations ◽  
Fmri Time Series

Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

2021 ◽  
Author(s):  
Carlos A. Severiano ◽  
Petrônio de Cândido de Lima e Silva ◽  
Miri Weiss Cohen ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Energy Systems ◽  
Fuzzy Time Series ◽  
Renewable Energy Systems ◽  
Spatio Temporal

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 Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series

2021 ◽  
Vol 88 ◽  
pp. 101627
Author(s):  
Ximeng Cheng ◽  
Zhiqian Wang ◽  
Xuexi Yang ◽  
Liyan Xu ◽  
Yu Liu
Keyword(s):  
Time Series ◽  
Human Activities ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Scale Detection

A systematic method for spatio-temporal phenology estimation of paddy rice using time series Sentinel-1 images

2021 ◽  
Vol 259 ◽  
pp. 112394
Author(s):  
Huijin Yang ◽  
Bin Pan ◽  
Ning Li ◽  
Wei Wang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Paddy Rice ◽  
Systematic Method ◽  
Spatio Temporal
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