scholarly journals Symplectic Geometry and Its Applications on Time Series Analysis

2020 ◽  
Author(s):  
Min Lei
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Symplectic Geometry ◽  
Principal Component ◽  
Symplectic Space ◽  
Hamiltonian Matrix ◽  
The Third ◽  
Series Analysis ◽  
Mode Decomposition ◽  
Basic Concepts

This chapter serves to introduce the symplectic geometry theory in time series analysis and its applications in various fields. The basic concepts and basic elements of mathematics relevant to the symplectic geometry are introduced in the second section. It includes the symplectic space, symplectic transformation, Hamiltonian matrix, symplectic principal component analysis (SPCA), symplectic geometry spectrum analysis (SGSA), symplectic geometry mode decomposition (SGMD), and symplectic entropy (SymEn), etc. In addition, it also briefly reviews the applications of symplectic geometry on time series analysis, such as the embedding dimension estimation, nonlinear testing, noise reduction, as well as fault diagnosis. Readers who are familiar with the mathematical preliminaries may omit the second section, i.e. the theory part, and go directly to the third section, i.e. the application part.

An overview of the posters presented at Watermatex 2000. I. New models/integrated urban wastewater systems/time series analysis

2001 ◽  
Vol 43 (7) ◽  
pp. 377-380
Author(s):  
B. De Clercq ◽  
J. Meirlaen ◽  
F. Verdonck ◽  
P. A. Vanrolleghem
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Principal Component ◽  
Neural Network Modeling ◽  
Urban Wastewater ◽  
Environmental Technology ◽  
Series Analysis ◽  
New Models ◽  
Wastewater Systems ◽  
Sixth Session

This paper presents an overview of the posters presented in the sessions 1, 6 and 9 of the Watermatex 2000 conference. The first session focused on the development of new models in different areas of environmental technology, e.g. wastewater, ground pollution, sewers, etc. The sixth session dealt with integrated urban wastewater systems. Session 9 focused on the application of neural network modeling and principal component analysis in time series analysis. Rewarded posters are mentioned and selected for full paper publication in this issue of Wat. Sci. Tech.

The Mechanical Fault Diagnosis Method Based on EMD and Time Series Analysis

2014 ◽  
Vol 602-605 ◽  
pp. 2330-2333 ◽  
Author(s):  
Jun Ma ◽  
Shi Hai Zhang
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Analysis Method ◽  
Series Analysis ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

It is the precondition of vibration fault diagnosis technology that appropriate signal analysis method is applied to separate mechanical fault character message from vibration monitoring signal. Based on the characteristics of multi-exciting, multi-model, non-stationary, nonlinear of the complex mechanical vibration signal, the EMD (Empirical Mode Decomposition) method is firstly applied to decompose the most refined IMF (Intrinsic Mode Function) components of vibration monitoring signal, and then the time series analysis method is applied to estimate power spectrums of IMF components and separate the fault character messages. The feasibility and advantage of the associated method are proved by analyzing the diesel engine crankshaft vibration monitoring signal in the paper.

Wavelet-bounded empirical mode decomposition for measured time series analysis

2018 ◽  
Vol 99 ◽  
pp. 14-29 ◽  
Author(s):  
Keegan J. Moore ◽  
Mehmet Kurt ◽  
Melih Eriten ◽  
D. Michael McFarland ◽  
Lawrence A. Bergman ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Mode Decomposition ◽  
Series Analysis ◽  
Mode Decomposition ◽  
Measured Time

scholarly journals Multivariate Time Series Analysis of Temperatures in the Archaeological Museum of L’Almoina (Valencia, Spain)

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4377
Author(s):  
Sandra Ramírez ◽  
Manuel Zarzo ◽  
Fernando-Juan García-Diego
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Thermal Conditions ◽  
Archaeological Site ◽  
Series Analysis ◽  
Long Term Monitoring ◽  
Term Monitoring

An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019–October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums.

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