Nonlinear Modeling And Forecasting

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Nonlinear Modeling And Forecasting

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Author : Martin Casdagli
language : en
Publisher: Westview Press
Release Date : 1992-06-20

Nonlinear Modeling And Forecasting written by Martin Casdagli and has been published by Westview Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-06-20 with Mathematics categories.

Based on a Santa Fe Institute and NATO sponsored workshop, this book brings together the ideas of leading researchers in the rapidly expanding, interdisciplinary field of nonlinear modeling in an attempt to stimulate the cross-fertilization of ideas and the search for unifying themes. The central theme of the workshop was the construction of nonlinear models from time-series data. Approaches to this problem have drawn from the disciplines of multivariate function approximation and neural nets, dynamical systems and chaos, statistics, information theory, and control theory. Applications have been made to economics, mechanical engineering, meteorology, speech processing, biology, and fluid dynamics.

Non Linear Time Series Models In Empirical Finance

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Author : Philip Hans Franses
language : en
Publisher: Cambridge University Press
Release Date : 2000-07-27

Non Linear Time Series Models In Empirical Finance written by Philip Hans Franses and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-07-27 with Business & Economics categories.

This 2000 volume reviews non-linear time series models, and their applications to financial markets.

Nonlinear Modeling And Forecasting

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Author : Mertin Casdagli
language : en
Publisher:
Release Date : 1992

Nonlinear Modeling And Forecasting written by Mertin Casdagli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.

System Identification With Matlab

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Author : A. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-19

System Identification With Matlab written by A. Smith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-19 with categories.

This book develops the work with Nonlinear Models and Time Series Identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. MATLAB System Identification Toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.. It is possible to analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values The most important content that this book provides are the following: - When to Fit Nonlinear Models - Nonlinear Model Estimation - Nonlinear Model Structures - Nonlinear ARX Models - Hammerstein-Wiener Models - Nonlinear Grey-Box Models - Preparing Data for Nonlinear Identification - Identifying Nonlinear ARX Models - Prepare Data for Identification - Configure Nonlinear ARX Model Structure - Specify Estimation Options for Nonlinear ARX Models - Initialize Nonlinear ARX Estimation Using Linear Model - Estimate Nonlinear ARX Models in the App - Estimate Nonlinear ARX Models at the Command Line - Estimate Nonlinear ARX Models Initialized Using Linear ARX Models - Validate Nonlinear ARX Models - Using Nonlinear ARX Models - Linear Approximation of Nonlinear Black-Box Models - Nonlinear Black-Box Model Identification - Identifying Hammerstein-Wiener Models - Available Nonlinearity Estimators for Hammerstein-Wiener Models - Estimate Hammerstein-Wiener Models in the App . - Estimate Hammerstein-Wiener Models at the Command Line - Validating Hammerstein-Wiener Models - How the Software Computes Hammerstein-Wiener Model Output - Evaluating Nonlinearities (SISO) - Evaluating Nonlinearities (MIMO) - Simulation of Hammerstein-Wiener Model - Estimate Hammerstein-Wiener Models Initialized Using Linear OE Models - Estimate Linear Grey-Box Models - Estimate Continuous-Time Grey-Box Model for Heat Diffusion - Estimate Discrete-Time Grey-Box Model with Parameterized Disturbance - Estimate Coefficients of ODEs to Fit Given Solution - Estimate Model Using Zero/Pole/Gain Parameters - Estimate Nonlinear Grey-Box Models - Identifying State-Space Models with Separate Process and Measurement Noise Descriptions - Time Series Identification - Preparing Time-Series Data - Estimate Time-Series Power Spectra - Estimate AR and ARMA Models - Definition of AR and ARMA Models - Estimating Polynomial Time-Series Models in the App - Estimating AR and ARMA Models at the Command Line - Estimate State-Space Time Series Models - Identify Time-Series Models at the Command Line - Estimate ARIMA Models - Analyze Time-Series Models - Introduction to Forecasting of Dynamic System Response - Forecasting Time Series Using Linear Models - Forecasting Response of Linear Models with Exogenous Inputs - Forecasting Response of Nonlinear Models - Forecast the Output of a Dynamic System - Forecast Time Series Data Using an ARMA Model - Recursive Model Identification

Modelling And Forecasting Financial Data

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Author : Abdol S. Soofi
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Modelling And Forecasting Financial Data written by Abdol S. Soofi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.

Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.

Nonlinear Modeling Of Solar Radiation And Wind Speed Time Series

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Author : Luigi Fortuna
language : en
Publisher: Springer
Release Date : 2016-06-21

Nonlinear Modeling Of Solar Radiation And Wind Speed Time Series written by Luigi Fortuna and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-21 with Technology & Engineering categories.

This brief is a clear, concise description of the main techniques of time series analysis —stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.— as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques. The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques. Results are well-illustrated by figures and tables.

Nonlinear Time Series

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Author : Jianqing Fan
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-11

Nonlinear Time Series written by Jianqing Fan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-11 with Mathematics categories.

This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.

Nonlinear Financial Econometrics Forecasting Models Computational And Bayesian Models

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Author : G. Gregoriou
language : en
Publisher: Springer
Release Date : 2010-12-21

Nonlinear Financial Econometrics Forecasting Models Computational And Bayesian Models written by G. Gregoriou and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-21 with Business & Economics categories.

This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.

Nonlinear Modeling For Forecasting Of Fish Production From Ponds

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Author : A. Rathore
language : en
Publisher:
Release Date : 1999

Nonlinear Modeling For Forecasting Of Fish Production From Ponds written by A. Rathore and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.

Extracting Knowledge From Time Series

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Author : Boris P. Bezruchko
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-03

Extracting Knowledge From Time Series written by Boris P. Bezruchko and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-03 with Science categories.

Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.