Stationary Intervals for Random Waves by Functional Clustering of Spectral Densities

2020 ◽  
Author(s):  
Diego Rivera-García ◽  
Luis Angel García-Escudero ◽  
Agustín Mayo-Iscar ◽  
Joaquin Ortega
Keyword(s):  
Time Series ◽  
Functional Data ◽  
Random Waves ◽  
Spectral Densities ◽  
Time Intervals ◽  
Functional Clustering ◽  
Wave Data ◽  
Model Method ◽  
Spline Basis ◽  
Consecutive Time

Abstract A new time series clustering procedure, based on Functional Data Analysis techniques applied to spectral densities, is employed in this work for the detection of stationary intervals in random waves. Long records of wave data are divided into 30-minute or one-hour segments and the spectral density of each interval is estimated by one of the standard methods available. These spectra are regarded as the main characteristic of each 30-minute time series for clustering purposes. The spectra are considered as functional data and, after representation on a spline basis, they are clustered by a mixtures model method based on a truncated Karhunen-Loéve expansion as an approximation to the density function for functional data. The clustering method uses trimming techniques and restrictions on the scatter within groups to reduce the effect of outliers and to prevent the detection of spurious clusters. Simulation examples show that the procedure works well in the presence of noise and the restrictions on the scatter are effective in avoiding the detection of false clusters. Consecutive time intervals clustered together are considered as a single stationary segment of the time series. An application to real wave data is presented.

Stationary Intervals for Random Waves by Functional Clustering of Spectral Densities

2021 ◽  
Author(s):  
Diego Rivera Garc\xeda ◽  
Luis Angel Garc\xeda Escudero ◽  
Agustin Mayo Iscar ◽  
Joaquin Ortega
Keyword(s):  
Random Waves ◽  
Spectral Densities ◽  
Functional Clustering

Time Series, Spectral Densities and Robust Functional Clustering

2018 ◽  
Vol 52 (1) ◽  
pp. 135-152
Author(s):  
D. Rivera-García ◽  
L. A. García-Escudero ◽  
A. Mayo-Iscar ◽  
J. Ortega
Keyword(s):  
Time Series ◽  
Spectral Densities ◽  
Functional Clustering

scholarly journals Regional Downscaling of Copernicus ERA5 Wave Data for Coastal Engineering Activities and Operational Coastal Services

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 859
Author(s):  
Giorgio Bellotti ◽  
Leopoldo Franco ◽  
Claudia Cecioni
Keyword(s):  
Time Series ◽  
National Level ◽  
Wave Model ◽  
Wave Climate ◽  
Tyrrhenian Sea ◽  
Computational Domain ◽  
Multiple Wave ◽  
Space Resolution ◽  
Wave Data ◽  
Fundamental Information

Hindcasted wind and wave data, available on a coarse resolution global grid (Copernicus ERA5 dataset), are downscaled by means of the numerical model SWAN (simulating waves in the nearshore) to produce time series of wave conditions at a high resolution along the Italian coasts in the central Tyrrhenian Sea. In order to achieve the proper spatial resolution along the coast, the finite element version of the model is used. Wave data time series at the ERA5 grid are used to specify boundary conditions for the wave model at the offshore sides of the computational domain. The wind field is fed to the model to account for local wave generation. The modeled sea states are compared against the multiple wave records available in the area, in order to calibrate and validate the model. The model results are in quite good agreement with direct measurements, both in terms of wave climate and wave extremes. The results show that using the present modeling chain, it is possible to build a reliable nearshore wave parameters database with high space resolution. Such a database, once prepared for coastal areas, possibly at the national level, can be of high value for many engineering activities related to coastal area management, and can be useful to provide fundamental information for the development of operational coastal services.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

scholarly journals Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

2018 ◽  
Vol 8 (10) ◽  
pp. 1766 ◽  
Author(s):  
Arthur Leroy ◽  
Andy MARC ◽  
Olivier DUPAS ◽  
Jean Lionel REY ◽  
Servane Gey
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Clustering Algorithms ◽  
Principal Component ◽  
Continuous Functions ◽  
Functional Principal Component Analysis ◽  
Data Set ◽  
Functional Clustering ◽  
Sport Science

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

scholarly journals SHORELINE AT JETTY DUE TO CYCLIC AND RANDOM WAVES

1988 ◽  
Vol 1 (21) ◽  
pp. 141
Author(s):  
Todd L. Walton ◽  
Philip L.F. Liu ◽  
Edward B. Hands
Keyword(s):  
Time Series ◽  
Sediment Transport ◽  
Numerical Model ◽  
Analytical Solution ◽  
Numerical Models ◽  
Random Waves ◽  
Dimensionless Form ◽  
Wave Direction ◽  
Continuous Cycling ◽  
Small Wave

This paper examines the effects of random and deterministic cycling of wave direction on the updrift beach planform adjacent to a jetty. Results provided using a simplified numerical model cast in dimensionless form indicate the importance of the time series of wave direction in determining design jetty length for a given net sediment transport. Continuous cycling of • wave direction leads to the expected analytical solution. Simplications in the numerical model used restrict the applications to small wave angles, no diffraction, no reflection of waves off structure, no refraction, and no sand bypassing at jetty. The concept can be extended to more sophisticated numerical models.

scholarly journals INFLUENCE OF THE PALEOCLIMATIC RECONSTRUCTIONS UNCERTAINTY ON THE SUBMARINE PERMAFROST DYNAMICS

2019 ◽  
Vol 4 (1) ◽  
pp. 99-105
Author(s):  
Valentina Malakhova ◽  
Alexey Eliseev
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Air Temperature ◽  
Climate Changes ◽  
Arctic Shelf ◽  
The Arctic ◽  
Time Intervals ◽  
Ocean Level ◽  
Subsea Permafrost ◽  
Uncertainty Coefficient

The estimates of the subsea permafrost sensitivity to the uncertainty of paleoclimatic reconstructions of air temperature and ocean level have been obtained. This was done by using the model for thermophysical processes in the subsea sediments and the scenario for climate changes at the Arctic shelf for the last 400 kyr. This model was forced by four time series of temperature at the sediment top, by using different combinations of air temperature and sea level. The uncertainty coefficient of the response of the permafrost base depth is less than 0,3, with the exception of isolated time intervals and / or the deepest areas of the shelf.

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