scholarly journals Let's do the time warp again: non‐linear time series matching as a tool for sequentially structured data in ecology

Ecosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
Author(s):  
Jens C. Hegg ◽  
Brian P. Kennedy
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Structured Data ◽  
Time Warp ◽  
Non Linear

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 Hybrid forecasting model for non-linear time series

2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Forecasting Model ◽  
Non Linear ◽  
Hybrid Forecasting

scholarly journals Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

2014 ◽  
Vol 41 (2) ◽  
pp. 249-258 ◽  
Author(s):  
Erol Egrioglu ◽  
Ufuk Yolcu ◽  
Cagdas Hakan Aladag ◽  
Eren Bas
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Linear Time ◽  
Time Series Forecasting ◽  
Non Linear ◽  
Artificial Neural

scholarly journals A non-linear time series prediction method for missing daily flow rate data of Middle Firat Catchment

2011 ◽  
Vol 6 ◽  
pp. 331-336
Author(s):  
A. Albostan ◽  
B. Barutcu ◽  
B. Onoz
Keyword(s):  
Time Series ◽  
Flow Rate ◽  
Linear Time ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Rate Data ◽  
Non Linear

Monitoring means and covariances of multivariate non linear time series with heavy tails

2017 ◽  
Vol 46 (21) ◽  
pp. 10394-10415 ◽  
Author(s):  
Robert Garthoff ◽  
Wolfgang Schmid
Keyword(s):  
Time Series ◽  
Heavy Tails ◽  
Linear Time ◽  
Non Linear

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (4) ◽  
pp. 1103-1109 ◽  
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

Non Linear Time Series. A Dynamical System Approach

Population ◽  
1992 ◽  
Vol 47 (5) ◽  
pp. 1320
Author(s):  
J.-M. Z. ◽  
Tong Howell
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
System Approach ◽  
Linear Time ◽  
Dynamical System Approach ◽  
Non Linear

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
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