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 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.

scholarly journals Contextualizing time-series data: Quantification of short-term regional variability in the San Pedro Channel using high-resolution in situ glider data

2017 ◽  
Author(s):  
Elizabeth N. Teel ◽  
Xiao Liu ◽  
Bridget N. Seegers ◽  
Matthew A. Ragan ◽  
William Z. Haskell ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Time Series Data ◽  
Series Data ◽  
Regional Variability ◽  
Subsurface Chlorophyll Maximum ◽  
San Pedro ◽  
Physical And Chemical

Abstract. Oceanic time-series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset was dominated by four water column profile types: active upwelling, offshore influence, subsurface chlorophyll maximum, and surface bloom. On average, waters across the SPC were most similar to offshore profiles. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC, and SPOT was least similar to SPC locations that were closest to the Palos Verdes Peninsula. Subsurface chlorophyll maxima with co-located chlorophyll and particle maxima were common in 2013 and 2014 suggesting that these subsurface chlorophyll maxima might contribute significantly to the local primary production. These results indicate that high-resolution in situ glider deployments can be used to determine the spatial domain of time-series data, allowing for broader application of these datasets and greater integration into modeling efforts.

Characterization of load curves in a real distribution system based on K-MEANS algorithm with time-series data

2020 ◽  
Author(s):  
Hernan R. Ullón ◽  
Luís F. Ugarte ◽  
Eduardo Lacusta Jr. ◽  
Madson C. de Almeida
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Power Systems ◽  
Smart Grids ◽  
Distribution Systems ◽  
Time Series Data ◽  
Series Data ◽  
Clustering Method ◽  
Large Databases

The modernization of conventional distribution systems in smart grids leads us to face new challenges when dealing with extremely large databases, commonly called Big Data. The accuracy and volume of data have grown significantly with the introduction of Advanced Measurement Infrastructure (AMI). This generates a data tsunami used in different applications of power systems creating great computational efforts, as is the case with the use of a large database of load curves. Due to the patterns that are repeated annually in the demand for active and reactive power in distribution systems, it is necessary to use load clustering methodologies. Based on historical load data, this paper represents a comprehensive approach that uses data mining based on the K-Means clustering method in time-series data for the characterization of real load curves. Besides, a comparative analysis will be presented considering three different distance measurements. This data mining process is presented as a promising method for the recognition of patterns allowing to reduce large databases to some characteristic curves to reduce the computational burden in various applications of power systems. This clustering method is tested using a real database of distribution transformers at UNICAMP.

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