scholarly journals Deterministic dynamics of the magnetosphere: results of the 0–1 test

2013 ◽  
Vol 20 (1) ◽  
pp. 11-18 ◽  
Author(s):  
S. Prabin Devi ◽  
S. B. Singh ◽  
A. Surjalal Sharma
Keyword(s):  
Magnetic Field ◽  
Time Series ◽  
Solar Wind ◽  
Time Series Data ◽  
Nonlinear Dynamical System ◽  
Series Data ◽  
Nonlinear Dynamical ◽  
Deterministic Dynamics ◽  
Time Period ◽  
Solar Wind Magnetic Field

Abstract. A test for deterministic dynamics in a time series data, namely the 0–1 test (Gottawald and Melbourne, 2004, 2005), is used to study the magnetospheric dynamics. The data, corresponding to the same time period, of the auroral electrojet index AL and the magnetic field component Bz of the solar wind magnetic field measured at 1 AU are used to compute the parameter K, which is zero for non-chaotic and unity for chaotic systems. For the magnetosphere and also for the turbulent solar wind, K has values corresponding to a nonlinear dynamical system with chaotic behaviour. This result is consistent with the Lyapunov exponents computed from the same time series data.

Approach to Forecasting Occurrence of Slope Failure with Nonlinear Dynamical System

2013 ◽  
Vol 438-439 ◽  
pp. 1597-1602
Author(s):  
Han Dong Liu
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Time Scale ◽  
Slope Failure ◽  
Time Series Data ◽  
Nonlinear Dynamical System ◽  
Series Data ◽  
Geologic Hazard ◽  
Nonlinear Dynamical ◽  
Short Term Prediction

Landslides constitute a major geologic hazard because they are widespread and commonly occur in connection with other major natural disasters such as earthquakes, rainstorms, wildfires and floods. Nonlinear dynamical system (NDS) techniques have been developed to analyze chaotic time series data. According to NDS theory, the correlation dimension and predictable time scale are evaluated from a single observed time series. The Xintan landslide case study is presented to demonstrate that chaos exists in the evolution of a landslide and the predictable time scale must be considered. The possibility for long-term, medium-term and short-term prediction of landslide is discussed.

Nonlinear Dynamical Modeling of Sonar Signal Propagation

1999 ◽  
Author(s):  
Richard A. Katz ◽  
Azizul H. Quazi
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Time Series Data ◽  
Signal Propagation ◽  
Series Data ◽  
New Paradigm ◽  
Nonlinear Dynamical ◽  
Ultrasonic Time ◽  
Nonlinear Performance ◽  
Traditional Approaches

Abstract Sonar signal propagation has been traditionally modeled as a linear stochastic process. This research is a major departure from that classical viewpoint and is aimed at modeling acoustic signal propagation dynamics deterministically using the analysis tools of Nonlinear Dynamics and Chaos Theory. Advantages of using this new paradigm over traditional approaches to sonar modeling are demonstrated by simulating a linear frequency modulated (LFM) active sonar signal and two nonlinear variations of it. When nonlinear performance metrics called the mutual information (MI) and the differential radius (DR) are applied to both linear and nonlinear variants of the LFM signal, both metrics are found to be useful detectors of nonlinearity in a simulated acoustic time series. Next we demonstrate the DR technique, on real ultrasonic time series data, that was found to be useful for the diagnosis and treatment of a heart disease called atrial fibrillation. After summarizing results, we conclude with recommended future directions in applied nonlinear sonar signal propagation research.

scholarly journals The Presidential Political Business Cycle of 1972

1984 ◽  
Vol 44 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Robert R. Keller ◽  
Ann Mari May
Keyword(s):  
Time Series ◽  
Business Cycle ◽  
Time Series Data ◽  
Political Business Cycle ◽  
Series Data ◽  
The Political ◽  
Operating Environment ◽  
Macroeconomic Conditions ◽  
Price Controls ◽  
Time Period

Previous studies of the political business cycle have examined time series data to determine whether a pattern of pre-election boom and post-election slump exists. The studies do not investigate the behavior and mechanisms by which a politician may effectuate a political business cycle. We focus on one time period, 1969 to 1972, and conclude that President Nixon's personality and operating environment explain why he manipulated the economy for political gain. The mechanisms he utilized to improve macroeconomic conditions before the 1972 election include monetary policy, fiscal policy, and wage-price controls.

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2020 ◽  
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Dynamical Features

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response in the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analyzed for the period of nine years using nonlinear dynamics tools (Maximal Lyapunov Exponent, MLE, Approximate Entropy, ApEn and Delay Vector Variance, DVV). We found a significant trend between each nonlinear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity response are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and declined further during major geomagnetic storms. However, the MLE and ApEn values obtained in VBs indicate that chaotic and dynamical complexity response are high with no significant difference between the periods that are associate with minor, moderate and major geomagnetic storms. The test for nonlinearity in the Dst time series during major geomagnetic storm reveals the strongest nonlinearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics is nonlinear and the solar wind dynamics is consistently stochastic in nature.

scholarly journals Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

2020 ◽  
Author(s):  
Robert Glenn Moulder ◽  
Elena Martynova ◽  
Steven M. Boker
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Time Series Data ◽  
Nonlinear Dynamical Systems ◽  
Series Data ◽  
Alternative View ◽  
Unified Framework ◽  
Nonlinear Dynamical ◽  
Depth Analysis ◽  
Analysis Of Time Series

Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.

Trend Analysis on Precipitation Time Series Data in Munda Catchment, Pakistan

2014 ◽  
Vol 692 ◽  
pp. 97-102 ◽  
Author(s):  
Ijaz Ahmad ◽  
De Shan Tang ◽  
Mei Wang ◽  
Sarfraz Hashim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Positive Trend ◽  
Precipitation Time Series ◽  
Significance Level ◽  
Annual Basis ◽  
Time Period ◽  
Seasonal Basis ◽  
Correlation Trend

This paper investigates the trends in precipitation time series of 10 stations for the time period of 51 years (1961-2011) in the Munda catchment, Pakistan. The Mann-Kendall (MK) and Spearman’s rho (SR) tests were employed for detection of the trend on the seasonal and annual basis at 5% significance level. For the removal of the serial correlation Trend Free Pre-Whitening approach was applied. The results show, a mixture of positive (increasing) and negative (decreasing) trends. A shift in precipitation time series is observed on seasonal scale from summer to autumn season. The Charbagh station exhibits the most number of significant cases on the seasonal basis while, no significant trends are found at Thalozom, Kalam and Dir stations. On the annual basis, only Charbagh station shows a significant positive trend, while on other stations, no significant trends are found annually. The performance of MK and SR tests was consistent in detecting the trend at different stations.

A Temporal Pattern Discovery Method and its Application to Physiological Data: A Case Study (Preprint)

2019 ◽  
Author(s):  
Catherine Inibhunu ◽  
Carolyn McGregor
Keyword(s):  
Time Series ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Mathematical Formulation ◽  
Series Data ◽  
Physiological Data ◽  
Time Period ◽  
Temporal Pattern Discovery

BACKGROUND High frequency data collected from monitors and sensors that provide measures relating to patients’ vital status in intensive care units (NICUs) has the potential to provide valuable insights which can be crucial when making critical decisions for the care of premature and ill term infants. However, this exercise is not trivial when faced with huge volumes of data that are captured every second at the bedside/home. The ability to collect, analyze and understand any hidden relationships in the data that may be vital for clinical decision making is a central challenge. OBJECTIVE The main goal of this research is to develop a method to detect and represent relationships that may exist in temporal abstractions (TA) and temporal patterns (TP) derived from time oriented data. The premise of this research is that in clinical care, the discovery of unknown relationships among physiological time oriented data can lead to detection of onset of conditions, aid in classifying abnormal or normal behaviors or derive patterns of an altered trajectory towards a problematic future state for a patient. That is, there is great potential to use this approach to uncover previously unknown pathophysiologies that are present in high speed physiological data. METHODS This research introduces a TPR process and an associated TPRMine algorithm which adopts a stepwise approach to temporal pattern discovery by first applying a scaled mathematical formulation of the time series data. This is achieved by modelling the problem space as a finite state machine representation where for a given timeframe, a time series data segment transitions from one state to another based on probabilistic weights and then quantifying the many paths a time series data may transition to. RESULTS The TPRMine Algorithm has been designed, implemented and applied to patient physiological data streams captured from the McMaster Children’s Hospital NICU. The algorithm has been applied to understand the number of states a patient in a NICU bed can transition to in a given time period and a demonstration of formulation of hypothesis tests. In addition, a quantification of these states is completed leading to creation of a vital scoring. With this, it’s possible to understand the percent of time a patient remains in a high or low vital score. CONCLUSIONS The developed method allows understanding the number of states a patient may transition to in any given time period. Adding some clinical context to the identified states facilitates state quantification allowing formulation of thresholds which leads to generating patient scores. This is an approach that can be utilized for identifying patient at risk of some clinical condition prior to disease progress. Additionally the developed method facilitates identification of frequent patterns that could be associated with generated thresholds.

scholarly journals A convergence based assessment of relative differences in age-stratified susceptibility and infectiousness for SARS-CoV-2 variants of B.1.1.7 lineage

2021 ◽  
Author(s):  
Sarah Dean Rasmussen
Keyword(s):  
Time Series ◽  
Environmental Effects ◽  
Total Population ◽  
Time Series Data ◽  
Relative Increase ◽  
Series Data ◽  
Gene Target ◽  
Modelling Framework ◽  
Time Period ◽  
Relative Differences

We propose (a) a method for aggregating and processing age-stratified subregional time series data for positive tests of infection given partial sampling for variant-of-concern biomarkers, and (b) a simple model-based theoretical framework for interpreting these processed data, to assess whether observed heterogeneity in age-specific relative differences can be explained by environmental effects alone. We then apply this strategy to public-domain subregional time series data with S-gene target failure (SGTF) sampling as a proxy for B.1.1.7 lineage, from weeks 45 to 50 of 2020 from England. For the time period in question, we observe convergence toward a 1.27 (95% CI 1.17-1.38) times higher ratio of SGTF to non-SGTF infection for 0-9-year-olds than for the total population, and a 1.16 (95% CI 1.09-1.23) times higher ratio for 10-19-year-olds. These are roughly comparable to previous findings, but this time we find high significance evidence for adequate compatibility with our proposed modelling framework criteria to conclude that these relative elevations for 0-19-year-olds are very unlikely to be explained by environmental effects alone. We also find possible indications that 0-19-year-olds might experience a higher relative increase in infectiousness than susceptibility for B.1.1.7.

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2021 ◽  
Vol 28 (2) ◽  
pp. 257-270
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Non Linear

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response to the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analysed for a period of 9 years using non-linear dynamics tools (maximal Lyapunov exponent, MLE; approximate entropy, ApEn; and delay vector variance, DVV). We found a significant trend between each non-linear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity responses are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and decline further during major geomagnetic storms. However, the MLE and ApEn values obtained from VBs indicate that chaotic and dynamical complexity responses are high with no significant difference between the periods that are associated with minor, moderate and major geomagnetic storms. The test for non-linearity in the Dst time series during major geomagnetic storm reveals the strongest non-linearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics are non-linear, and the solar wind dynamics are consistently stochastic in nature.

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