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Prediction of nonlinear nonstationary time series data

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Inter-area Oscillations in Power Systems

The study of complex dynamic processes governed by nonlinear and nonstationary characteristics is a problem of great importance in the;

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Modelling Nonlinear Economic Time Series

-economic data. Since estimation of nonlinear time series models is carried out using numerical algorithms, the book contains a chapter on;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

of improvements in building chaotic models are presented: nonlinear time series analysis, multi-step prediction, phase space dimensionality reduction;

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Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation and Ensembles

of improvements in building chaotic models are presented: nonlinear time series analysis, multi-step prediction, phase space dimensionality reduction;

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Identification and Inference for Econometric Models

. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data;

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Identification and Inference for Econometric Models

. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data;

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The Econometric Analysis of Seasonal Time Series

stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed;

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Nonlinear Time Series Analysis

A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series;

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Forecasting in Business and Economics

in economics and business. This is the only time series book to concentrate on the forecasting of economic data and to cover such a broad range;

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Time Series Analysis

first edition. In particular, sections on nonstationary panel data analysis and a discussion on the distinction between deterministic and;

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Themes in Modern Econometrics

stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed;

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Time Series Modeling of Neuroscience Data

Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG;

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Nonlinear Time Series

Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention;

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Nonlinear Time Series

Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention;

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Nonlinear Modelling of High Frequency Financial Time Series

Nonlinear Modelling of High Frequency Financial Time Series Edited by Christian Dunis and Bin Zhou In the competitive and risky environment;

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Athens Conference on Applied Probability and Time Series Analysis

distribution, frequency domain methods, long range dependent processes, nonstationary processes, and nonlinear time series. The methods presented can;

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Financial Surveillance

different control charts and different linear and nonlinear time series models and applies methods to real data from worldwide markets, as well as;

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Neural Networks for Applied Sciences and Engineering

for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for;

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Engineering Applications Of Correlation And Spectral Analysis

Expanded to cover more advanced applications where statistical properties of data can be nonstationary and the physical systems nonlinear;

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Introduction To Statistical Time Series

Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state;

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Complexity of Seismic Time Series

Complexity of Seismic Time Series: Measurement and Application applies the tools of nonlinear dynamics to seismic analysis, allowing;

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Practical Time Series Analysis Prediction with Statistics and Machine Learning

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the;

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Time Series

, the fitting of time series models, as well as computational facets of forecasting. Many time series of interest are nonlinear in which case;

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Time Series

, the fitting of time series models, as well as computational facets of forecasting. Many time series of interest are nonlinear in which case;

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Wavelet Neural Networks

financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and;

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Inference and Prediction in Large Dimensions

This book offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and;

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