scholarly journals Investigation of chaotic behavior for press time series of oil pipeline

2008 ◽  
Vol 57 (11) ◽  
pp. 6868
Author(s):  
Liu Jin-Hai ◽  
Zhang Hua-Guang ◽  
Feng Jian
Keyword(s):  
Time Series ◽  
Chaotic Behavior ◽  
Oil Pipeline ◽  
Press Time

scholarly journals Nonlinear analysis of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea

2018 ◽  
Vol 25 (2) ◽  
pp. 291-300 ◽  
Author(s):  
Berenice Rojo-Garibaldi ◽  
David Alberto Salas-de-León ◽  
María Adela Monreal-Gómez ◽  
Norma Leticia Sánchez-Santillán ◽  
David Salas-Monreal
Keyword(s):  
Time Series ◽  
Gulf Of Mexico ◽  
Nonlinear Analysis ◽  
Chaotic Behavior ◽  
Caribbean Sea ◽  
Historical Record ◽  
Nonlinear Analyses ◽  
The North ◽  
The Caribbean ◽  
The Individual

Abstract. Hurricanes are complex systems that carry large amounts of energy. Their impact often produces natural disasters involving the loss of human lives and materials, such as infrastructure, valued at billions of US dollars. However, not everything about hurricanes is negative, as hurricanes are the main source of rainwater for the regions where they develop. This study shows a nonlinear analysis of the time series of the occurrence of hurricanes in the Gulf of Mexico and the Caribbean Sea obtained from 1749 to 2012. The construction of the hurricane time series was carried out based on the hurricane database of the North Atlantic basin hurricane database (HURDAT) and the published historical information. The hurricane time series provides a unique historical record on information about ocean–atmosphere interactions. The Lyapunov exponent indicated that the system presented chaotic dynamics, and the spectral analysis and nonlinear analyses of the time series of the hurricanes showed chaotic edge behavior. One possible explanation for this chaotic edge is the individual chaotic behavior of hurricanes, either by category or individually regardless of their category and their behavior on a regular basis.

scholarly journals Chaotic Patterns in Aeroelastic Signals

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
F. D. Marques ◽  
R. M. G. Vasconcellos
Keyword(s):  
Time Series ◽  
State Space ◽  
Complex Dynamics ◽  
Chaotic Behavior ◽  
Separated Flow ◽  
Direct Analysis ◽  
Surrogate Data ◽  
The State ◽  
Aeroelastic System ◽  
Value Decomposition

This work presents the analysis of nonlinear aeroelastic time series from wing vibrations due to airflow separation during wind tunnel experiments. Surrogate data method is used to justify the application of nonlinear time series analysis to the aeroelastic system, after rejecting the chance for nonstationarity. The singular value decomposition (SVD) approach is used to reconstruct the state space, reducing noise from the aeroelastic time series. Direct analysis of reconstructed trajectories in the state space and the determination of Poincaré sections have been employed to investigate complex dynamics and chaotic patterns. With the reconstructed state spaces, qualitative analyses may be done, and the attractors evolutions with parametric variation are presented. Overall results reveal complex system dynamics associated with highly separated flow effects together with nonlinear coupling between aeroelastic modes. Bifurcations to the nonlinear aeroelastic system are observed for two investigations, that is, considering oscillations-induced aeroelastic evolutions with varying freestream speed, and aeroelastic evolutions at constant freestream speed and varying oscillations. Finally, Lyapunov exponent calculation is proceeded in order to infer on chaotic behavior. Poincaré mappings also suggest bifurcations and chaos, reinforced by the attainment of maximum positive Lyapunov exponents.

scholarly journals The comparative study of chaoticity and dynamical complexity of the low-latitude ionosphere, over Nigeria, during quiet and disturbed days

2014 ◽  
Vol 21 (1) ◽  
pp. 127-142 ◽  
Author(s):  
B. O. Ogunsua ◽  
J. A. Laoye ◽  
I. A. Fuwape ◽  
A. B. Rabiu
Keyword(s):  
Time Series ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Chaotic Behavior ◽  
Surrogate Data ◽  
Tsallis Entropy ◽  
Electron Content ◽  
Total Electron ◽  
Dynamical Complexity ◽  
Definite Pattern

Abstract. The deterministic chaotic behavior and dynamical complexity of the space plasma dynamical system over Nigeria are analyzed in this study and characterized. The study was carried out using GPS (Global Positioning System) TEC (Total Electron Content) time series, measured in the year 2011 at three GPS receiver stations within Nigeria, which lies within the equatorial ionization anomaly region. The TEC time series for the five quietest and five most disturbed days of each month of the year were selected for the study. The nonlinear aspect of the TEC time series was obtained by detrending the data. The detrended TEC time series were subjected to various analyses for phase space reconstruction and to obtain the values of chaotic quantifiers like Lyapunov exponents, correlation dimension and also Tsallis entropy for the measurement of dynamical complexity. The observations made show positive Lyapunov exponents (LE) for both quiet and disturbed days, which indicates chaoticity, and for different days the chaoticity of the ionosphere exhibits no definite pattern for either quiet or disturbed days. However, values of LE were lower for the storm period compared with its nearest relative quiet periods for all the stations. The monthly averages of LE and entropy also show no definite pattern for the month of the year. The values of the correlation dimension computed range from 2.8 to 3.5, with the lowest values recorded at the storm period of October 2011. The surrogate data test shows a significance of difference greater than 2 for all the quantifiers. The entropy values remain relatively close, with slight changes in these values during storm periods. The values of Tsallis entropy show similar variation patterns to those of Lyapunov exponents, with a lot of agreement in their comparison, with all computed values of Lyapunov exponents correlating with values of Tsallis entropy within the range of 0.79 to 0.81. These results show that both quantifiers can be used together as indices in the study of the variation of the dynamical complexity of the ionosphere. The results also show a strong play between determinism and stochasticity. The behavior of the ionosphere during these storm and quiet periods for the seasons of the year are discussed based on the results obtained from the chaotic quantifiers.

QUASIPERIODIC AND CHAOTIC BEHAVIOR ON ZAKHAROV EQUATIONS

1994 ◽  
Vol 08 (13) ◽  
pp. 833-840
Author(s):  
C. Y. ZHENG ◽  
X. T. HE ◽  
Y. TAN
Keyword(s):  
Time Series ◽  
Spatial Patterns ◽  
Modulation Instability ◽  
Chaotic Behavior ◽  
Spatiotemporal Chaos ◽  
Unstable Wave ◽  
Potential Source ◽  
Wave Numbers

It is first shown from the modulation instability of the conserved Zakharov equations (ZE) that spatiotemporal chaos is formed in the different regions of the unstable wave numbers. We find that chaos sets in when two homoclinic structures are present and then many and continual homoclinic crossings occur throughout the time series. It is shown that the presence of a homoclinic structure is a potential source of both temporal chaos and spatial patterns.

CHARACTERIZATION OF CHAOTICITY IN THE TRANSIENT CURRENT THROUGH PMMA THIN FILMS

Fractals ◽  
2006 ◽  
Vol 14 (02) ◽  
pp. 125-131 ◽  
Author(s):  
A. HACINLIYAN ◽  
Y. SKARLATOS ◽  
H. A. YILDIRIM ◽  
G. SAHIN
Keyword(s):  
Thin Films ◽  
Time Series ◽  
Lyapunov Exponent ◽  
Power Law ◽  
Chaotic Behavior ◽  
Transient Current ◽  
Positive Lyapunov Exponent ◽  
Aluminum Films ◽  
Thin Aluminum

Chaotic behavior in the transient current through thin Aluminum-PMMA-Aluminum films has been analyzed for times ranging up to 30,000s, in the temperature range 293–363K for applied voltages in the range 10–80V. Time series analysis reveals a positive Lyapunov exponent consistently and reproducibly throughout this range. Power law relaxation as reflected by the autocorrelation function and the positive Lyapunov exponent show parallel behaviors as a function of applied electric field.

scholarly journals Deterministic chaos and forecasting in Amazon’s share prices

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 253-273
Author(s):  
Michael Hanias ◽  
Stefanos Tsakonas ◽  
Lykourgos Magafas ◽  
Eleftherios I. Thalassinos ◽  
Loukas Zachilas
Keyword(s):  
Time Series ◽  
Chaos Theory ◽  
Stock Prices ◽  
Stock Price ◽  
Chaotic Behavior ◽  
Financial Time Series ◽  
Deterministic Chaos ◽  
Value Added ◽  
Share Prices ◽  
Daily Data

Research background: The application of non-linear analysis and chaos theory modelling on financial time series in the discipline of Econophysics. Purpose of the article: The main aim of the article is to identify the deterministic chaotic behavior of stock prices with reference to Amazon using daily data from Nasdaq-100. Methods: The paper uses nonlinear methods, in particular chaos theory modelling, in a case study exploring and forecasting the daily Amazon stock price. Findings & Value added: The results suggest that the Amazon stock price time series is a deterministic chaotic series with a lot of noise. We calculated the invariant parameters such as the maxi-mum Lyapunov exponent as well as the correlation dimension, managed a two-days-ahead forecast through phase space reconstruction and a grouped data handling method.

scholarly journals The Dynamics of Philippine Foreign Exchange Rates (2013-2017): A Test for Chaos

2019 ◽  
Vol 8 (11) ◽  
pp. 486-489
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Lyapunov Exponent ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Chaotic Behavior ◽  
The Philippines ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
Foreign Exchange Rates

In most studies on dynamics of time series financial data, the absence of chaotic behavior is generally observed. However, this theory is not yet established in the dynamics of foreign exchange rates. Conflicting claims of presence and absence of chaos in foreign exchange rates open door for further investigation considering various deterministic factors. This work examines the dynamics of exchange rate of the Philippine Peso against selected foreign currencies. Time series data were collected for eight (8) of Philippine’s top trading partners as categorized according to economic condition. The data obtained with permission from the Central Bank of the Philippines covered the years 2013 to 2017. Data sets were plotted revealing non-linear movement of Philippine exchange rates against time. The foreign exchange rate time series obtained per currency were examined for chaotic behavior by computing the Largest Lyapunov Exponents (LLE). A positive Lyapunov exponent is an indication of sensitivity dependence, i.e, a chaotic dynamics; whereas, a negative Lyapunov exponent indicates otherwise. Computed LLE’s varied per currency but all were found to be negative. Therefore, using the Largest Lyapunov Exponent Test (LLE), analysis of the time series of Philippine foreign exchange rates shows little evidence of chaotic patterns.

scholarly journals Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

2021 ◽  
Vol 12 ◽  
Author(s):  
Suran Liu ◽  
Yujie You ◽  
Zhaoqi Tong ◽  
Le Zhang
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Flow Behavior ◽  
Series Data ◽  
High Dimensional ◽  
Future State ◽  
Disease Occurrence ◽  
The Future

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

Electric Load Demand and Electricity Prices ForecastingUsing Higher Order Neural Networks Trained by Kalman Filtering

2009 ◽  
pp. 295-313 ◽  
Author(s):  
Edgar N. Sanchez ◽  
Alma Y. Alanis ◽  
Jesús Rico
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Kalman Filtering ◽  
Chaotic Behavior ◽  
Higher Order ◽  
Electric Load ◽  
Electricity Prices ◽  
Load Demand ◽  
Higher Order Neural Networks

In this chapter, we propose the use of Higher Order Neural Networks (HONNs) trained with an extended Kalman filter based algorithm to predict the electric load demand as well as the electricity prices, with beyond a horizon of 24 hours. Due to the chaotic behavior of the electrical markets, it is not advisable to apply the traditional forecasting techniques used for time series; the results presented here confirm that HONNs can very well capture the complexity underlying electric load demand and electricity prices. The proposed neural network model produces very accurate next day predictions and also, prognosticates with very good accuracy, a week-ahead demand and price forecasts.

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