scholarly journals Characterization of Biologically Relevant Network Structures form Time-series Data

2018 ◽  
Author(s):  
Zoltan A. Tuza ◽  
Guy-Bart Stan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Network Structures ◽  
Biologically Relevant

Characterization of Time Series Data

2017 ◽  
pp. 211-230
Author(s):  
W. Kulp Christopher ◽  
J. Niskala Brandon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data

scholarly journals Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

2010 ◽  
Vol 2 (2) ◽  
pp. 388-415 ◽  
Author(s):  
Willem J.D. Van Leeuwen ◽  
Jennifer E. Davison ◽  
Grant M. Casady ◽  
Stuart E. Marsh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Vegetation Communities ◽  
Climate Time Series ◽  
Sky Island

scholarly journals CHARACTERIZATION OF LAND COVER SEASONALITY IN SENTINEL-1 TIME SERIES DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 97-104
Author(s):  
C. Dubois ◽  
M. M. Mueller ◽  
C. Pathe ◽  
T. Jagdhuber ◽  
F. Cremer ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Coniferous Forest ◽  
Series Data ◽  
Urban Land Cover ◽  
Multi Temporal ◽  
Forested Areas

Abstract. In this study, we analyze Sentinel-1 time series data to characterize the observed seasonality of different land cover classes in eastern Thuringia, Germany and to identify multi-temporal metrics for their classification. We assess the influence of different polarizations and different pass directions on the multi-temporal backscatter profile. The novelty of this approach is the determination of phenological parameters, based on a tool that has been originally developed for optical imagery. Furthermore, several additional multitemporal metrics are determined for the different classes, in order to investigate their separability for potential multi-temporal classification schemes. The results of the study show a seasonality for vegetation classes, which differs depending on the considered class: whereas pastures and broad-leaved forests show a decrease of the backscatter in VH polarization during summer, an increase of the backscatter in VH polarization is observed for coniferous forest. The observed seasonality is discussed together with meteorological information (precipitation and air temperature). Furthermore, a dependence of the backscatter of the pass direction (ascending/descending) is observed particularly for the urban land cover classes. Multi-temporal metrics indicate a good separability of principal land cover classes such as urban, agricultural and forested areas, but further investigation and use of seasonal parameters is needed for a distinct separation of specific forest sub-classes such as coniferous and deciduous.

scholarly journals Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

2017 ◽  
Author(s):  
Sacha Epskamp ◽  
Claudia van Borkulo ◽  
Date C. van der Veen ◽  
Michelle Servaas ◽  
Adela-Maria Isvoranu ◽  
...  
Keyword(s):  
Time Series ◽  
Partial Correlation ◽  
Time Series Data ◽  
Recent Literature ◽  
Series Data ◽  
Network Structures ◽  
Cross Sectional ◽  
Correlation Networks ◽  
Relevant Variables ◽  
Analysis Of Time Series

Recent literature has introduced (1) the network perspective to psychology, and (2) collection of time-series data in order to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intra-individual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time-series data. We explain the importance of partial correlation networks and exemplify the network structures on time-series data of a psychiatric patient.

Network models of psychopathology and comorbidity: Philosophical and pragmatic considerations

2010 ◽  
Vol 33 (2-3) ◽  
pp. 159-160 ◽  
Author(s):  
S. Brian Hood ◽  
Benjamin J. Lovett
Keyword(s):  
Time Series ◽  
Individual Differences ◽  
Time Series Data ◽  
Network Models ◽  
Psychological Disorders ◽  
Series Data ◽  
Network Structures ◽  
Philosophical View ◽  
Individual Time Series ◽  
The Individual

AbstractCramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

scholarly journals Hydrodynamic Characterization of Mugla Karst Aquifer Using Correlation and Spectral Analyses on the Rainfall and Springs Water-Level Time Series

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 85
Author(s):  
Çağdaş Sağır ◽  
Bedri Kurtuluş ◽  
Moumtaz Razack
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Karst Aquifer ◽  
Series Data ◽  
Karst Aquifers ◽  
Wet Season ◽  
Spectral Analyses ◽  
Aquifer System

Karst aquifers have been an important research topic for hydrologists for years. Due to their high storage capacity, karst aquifers are an important source of water for the environment. On the other hand, it is safety-critical because of its role in floods. Mugla Karst Aquifer (SW, Turkey) is the only major water-bearing formation in the close environs of Mugla city. Flooding in the wet season occurs every year in the recharge plains. The aquifer discharges by the seaside springs in the Akyaka district which is the main touristic point of interest in the area. Non-porous irregular internal structures make the karsts more difficult to study. Therefore, many different methodologies have been developed over the years. In this study, unit hydrograph analysis, correlation and spectral analyses were applied on the rainfall and spring water-level time series data. Although advanced karst formations can be seen on the surface like the sinkholes, it has been revealed that the interior structure is not highly karstified. 100–130 days of regulation time was found. This shows that the Mugla Karst has quite inertial behavior. Yet, the storage of the aquifer system is quite high, and the late infiltration effect caused by alluvium plains was detected. This characterization of the hydrodynamic properties of the Mugla karst system represents an important step to consider the rational exploitation of its water resources in the near future.

scholarly journals Near Real-Time Characterization of Spatio-Temporal Precursory Evolution of a Rockslide from Radar Data: Integrating Statistical and Machine Learning with Dynamics of Granular Failure

2019 ◽  
Vol 11 (23) ◽  
pp. 2777 ◽  
Author(s):  
Sourav Das ◽  
Antoinette Tordesillas
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Granular Media ◽  
Time Series Data ◽  
Surface Displacement ◽  
Radar Data ◽  
Series Data ◽  
Landslide Risk ◽  
Risk Of Failure

This study builds on fundamental knowledge of granular failure dynamics to develop a statistical and machine learning approach for characterization of a landslide. We demonstrate our approach for a rockslide using surface displacement data from a ground based radar monitoring system. The algorithm has three key components: (i) identification of a regime change point t 0 marking the departure from statistical invariance of the global velocity field, (ii) characterization of the clustering pattern formed by the velocity time series at t 0 , and (iii) classification of velocity patterns for t > t 0 to deliver a measure of risk of failure from t 0 and estimates of the time of emergent and imminent risk of failure. Unlike the prevailing approach of analysing time series data from one or a few chosen locations, we make full use of data from all monitored points on the slope (here 1803). We do not make a priori assumptions on the monitored domain and base our characterization of the complex spatial patterns and associated dynamics only from the data. Our approach is informed by recent developments in the physics and micromechanics of failure in granular media and is configured to accommodate additional data on landslide triggers and other determinants of landslide risk readily.

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