Spatial modeling for risk assessment of extreme values from environmental time series: a Bayesian nonparametric approach

2012 ◽  
Vol 23 (8) ◽  
pp. 649-662 ◽  
Author(s):  
Athanasios Kottas ◽  
Ziwei Wang ◽  
Abel Rodríguez
Keyword(s):  
Risk Assessment ◽  
Time Series ◽  
Spatial Modeling ◽  
Extreme Values ◽  
Bayesian Nonparametric ◽  
Nonparametric Approach ◽  
Environmental Time Series

scholarly journals A Bayesian Nonparametric Approach for Time Series Clustering

2014 ◽  
Vol 9 (1) ◽  
pp. 147-170 ◽  
Author(s):  
Luis E. Nieto-Barajas ◽  
Alberto Contreras-Cristán
Keyword(s):  
Time Series ◽  
Bayesian Nonparametric ◽  
Time Series Clustering ◽  
Nonparametric Approach

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

Cross-sample entropy estimation for time series analysis: a nonparametric approach

2021 ◽  
Author(s):  
Ignacio Ramírez-Parietti ◽  
Javier E. Contreras-Reyes ◽  
Byron J. Idrovo-Aguirre
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sample Entropy ◽  
Entropy Estimation ◽  
Nonparametric Approach ◽  
Series Analysis

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

2021 ◽  
pp. 036119812199459
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Performance Indicators ◽  
Federal Funding ◽  
Percentage Error ◽  
Bayesian Nonparametric ◽  
Transportation Services ◽  
Nonparametric Approach ◽  
Nonparametric Models ◽  
Bayesian Nonparametric Models ◽  
Average Annual Daily Traffic ◽  
The Mean

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

scholarly journals Feature-preserving interpolation and filtering of environmental time series

2015 ◽  
Vol 72 ◽  
pp. 71-76 ◽  
Author(s):  
Gregoire Mariethoz ◽  
Niklas Linde ◽  
Damien Jougnot ◽  
Hassan Rezaee
Keyword(s):  
Time Series ◽  
Feature Preserving ◽  
Environmental Time Series

scholarly journals Precision of continuous GPS velocities from statistical analysis of synthetic time series

2018 ◽  
Author(s):  
Christine Masson ◽  
Stephane Mazzotti ◽  
Philippe Vernant
Keyword(s):  
Time Series ◽  
Extreme Values ◽  
Regression Tree ◽  
Simple Method ◽  
Position Time Series ◽  
Gps Velocities ◽  
Synthetic Time Series ◽  
Continuous Gps ◽  
Automatic Treatment ◽  
Gps Noise

Abstract. We use statistical analyses of synthetic position time series to estimate the potential precision of GPS velocities. The synthetic series represent the standard range of noise, seasonal, and position offset characteristics, leaving aside extreme values. This analysis is combined with a new simple method for automatic offset detection that allows an automatic treatment of the massive dataset. Colored noise and the presence of offsets are the primary contributor to velocity variability. However, regression tree analyses show that the main factors controlling the velocity precision are first the duration of the series, followed by the presence of offsets and the noise (dispersion and spectral index). Our analysis allows us to propose guidelines, which can be applied to actual GPS data, that constrain the velocity accuracies (expressed as 95 % confidence limits) based on simple parameters: (1) Series durations over 8.0 years result in high velocity accuracies in the horizontal (0.2 mm yr−1) and vertical (0.5 mm yr−1); (2) Series durations of less than 4.5 years cannot be used for high-precision studies since the horizontal accuracy is insufficient (over 1.0 mm yr−1); (3) Series of intermediate durations (4.5–8.0 years) are associated with an intermediate horizontal accuracy (0.6 mm yr-1) and a poor vertical one (1.3 mm yr−1), unless they comprise no offset. Our results suggest that very long series durations (over 15–20 years) do not ensure a better accuracy compare to series of 8–10 years, due to the noise amplitude following a power-law dependency on the frequency. Thus, better characterizations of long-period GPS noise and pluri-annual environmental loads are critical to further improve GPS velocity precisions.

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