Linear Time Series Analysis

2017 ◽  
pp. 121-181
Author(s):  
Daniel Durstewitz
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Time ◽  
Series Analysis

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

scholarly journals Gait assessment with non-linear time series analysis of convalescence cerebrovascular attack patients

2015 ◽  
Vol 5 (1) ◽  
pp. 25-29
Author(s):  
TATSUHIKO FUKAHORI ◽  
HIROYA MIYABARA ◽  
MASAAKI AYABE
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Time ◽  
Series Analysis ◽  
Gait Assessment ◽  
Non Linear

scholarly journals Fiscal Re-Adjustments in the U.S.: A Non-Linear Time Series Analysis

2005 ◽  
Author(s):  
Andrea Cipollini ◽  
Bassam Fattouh ◽  
Kostas Mouratidis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Time ◽  
Series Analysis ◽  
Non Linear ◽  
The U.S

Influence of coarse particulate matter on chickenpox in Jiading District, Shanghai, 2009–2018: A distributed lag non-linear time series analysis

2020 ◽  
Vol 190 ◽  
pp. 110039
Author(s):  
Hongjie Yu ◽  
Yingjian Wang ◽  
Qian Peng ◽  
Yueqin Shao ◽  
Chunmei Duan ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Time Series Analysis ◽  
Linear Time ◽  
Series Analysis ◽  
Distributed Lag ◽  
Non Linear ◽  
Coarse Particulate Matter

Effect of a national 4C antibiotic stewardship intervention on the clinical and molecular epidemiology of Clostridium difficile infections in a region of Scotland: a non-linear time-series analysis

2017 ◽  
Vol 17 (2) ◽  
pp. 194-206 ◽  
Author(s):  
Timothy Lawes ◽  
José-María Lopez-Lozano ◽  
Cesar A Nebot ◽  
Gillian Macartney ◽  
Rashmi Subbarao-Sharma ◽  
...  
Keyword(s):  
Time Series ◽  
Clostridium Difficile ◽  
Time Series Analysis ◽  
Molecular Epidemiology ◽  
Antibiotic Stewardship ◽  
Linear Time ◽  
Series Analysis ◽  
Non Linear

A non-linear time-series analysis of the harmonic tremor observed at Shinmoedake volcano, Japan

2018 ◽  
Vol 216 (3) ◽  
pp. 1768-1784
Author(s):  
Yuki Natsume ◽  
Mie Ichihara ◽  
Minoru Takeo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Linear Time ◽  
Series Analysis ◽  
Non Linear
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