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Time Series and nonlinear time series

A modified probabilistic neural network (PNN) for nonlinear time series analysis

A modified probabilistic neural network (PNN) for nonlinear time series analysis

... The main purpose of this paper is to show how the Probabilistic Neural Network (PNN) architecture proposed by Specht can be easily adapted for nonlinear time series analysis[r] ...

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Nonlinear time series: semiparametric and nonparametric methods

Nonlinear time series: semiparametric and nonparametric methods

... of nonlinear time series ...nonparametric time series models. Such parametric nonlinear models and related methods may be too restrictive in many ...model nonlinear ...

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Wavelet analysis for non-stationary, nonlinear time series

Wavelet analysis for non-stationary, nonlinear time series

... QBO time series arises from the westerly shear zone descending more regularly than the easterly shear ...QBO time series are not well cap- tured by linear methods such as linear principal ...

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Advances in Statistical Network Modeling and Nonlinear Time Series Modeling

Advances in Statistical Network Modeling and Nonlinear Time Series Modeling

... of nonlinear models has been a notoriously di cult problem, especially for large dimensional data[82, 81, ...nonparametric nonlinear time series models that have been applied successfully to ...

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Concepts and tools for nonlinear time series modelling

Concepts and tools for nonlinear time series modelling

... methods. The paper concentrates on a selection of application-oriented tools and concepts which covers all the above-mentioned domains. Original exam- ples and illustrations are given throughout the text. Most of the ...

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Estimation for nonlinear time series models using estimating equations

Estimation for nonlinear time series models using estimating equations

... In this section we recall Godambe's (1985) theorem on stochastic processes and apply it to obtain optimal estimates for recently proposed nonlinear time series models.. Let {Yt1 td} be a[r] ...

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Search for Additive Nonlinear Time Series Causal Models

Search for Additive Nonlinear Time Series Causal Models

... of nonlinear time series causal ...linear time series to the simulated ...linear time series with contemporaneous causal relations, the linear procedure used for ...

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Fractional cointegration analysis of nonlinear time series with long memory

Fractional cointegration analysis of nonlinear time series with long memory

... of nonlinear models, where observables are non-trivial transformations of underlying stationary long memory ...observable time series follow a statistical factor model, and the underlying components ...

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Autoregressive nonlinear time-series modeling of traffic fatalities in Europe

Autoregressive nonlinear time-series modeling of traffic fatalities in Europe

... The comparison of time series of road safety among different countries has been an interesting research topic. Lassarre [22] applies the local linear trend model to ten European countries and uses the ...

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Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation

Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation

... F } I F , F ) , … , F G , F+ G , F+ G , G ) , F G r , … , F H is the extended observed series. Here F+ G and F+ G are the estimates of F G and F G from the previous steps. G ) is a normal random variable ...

8

A flexible approach to parametric inference in nonlinear time series models

A flexible approach to parametric inference in nonlinear time series models

... Ιν τερmσ οφ Θ τηε ϖαριανχε ιν τηε στατε εθυατιον ιτ ισ χεντερεδ αππροξιmατελψ οϖερ 0:1 ανδ, τηυσ, ωε αρε αλλοωινγ φορ εϖερψτηινγ φροm ϖερψ σmαλλ το mοδερατελψ λαργε σηιφτσ ιν τηε ΑΡ χοε′[r] ...

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A local field correlated and Monte Carlo based shallow neural network model for nonlinear time series prediction

A local field correlated and Monte Carlo based shallow neural network model for nonlinear time series prediction

... Time series forecasting has remained a challenging prob-lem in environmental research. Since the late 1980, not only shallow neural networks but also multi-layer perceptions (MLP) have been widely deployed ...

7

Noise reduction in nonlinear time series analysis

Noise reduction in nonlinear time series analysis

... chaotic time series has been ...chaotic time series using invariants such as fractal dimensions or Lyapunov ...such time series using the type o f methods discussed in chapter ...

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Essays in high dimensional nonlinear time series analysis

Essays in high dimensional nonlinear time series analysis

... resenting series in frequency domain can expose the periodicities of the series and can aid in understanding the data generating ...a time series as a measure of non-Gaussianity and ...

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A flexible approach to parametric inference in nonlinear and time varying time series models

A flexible approach to parametric inference in nonlinear and time varying time series models

... Abstract: Many structural break and regime-switching models have been used with macroeconomic and …nancial data. In this paper, we develop an extremely ‡exible paramet- ric model which can accommodate virtually any of ...

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Nonlinearity In Exchange Rates and Forecasting

Nonlinearity In Exchange Rates and Forecasting

... The time series models are developed mainly for dynamic analysis and ...rates. Time series models, however, are often criticized because of their atheoretical approach in modeling economic ...

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Stationary Time Series in Pricing

Stationary Time Series in Pricing

... forecasting time series for solving the pricing problem in the enterprise were ...best time series approximation model has been formed based on the ...the time series, which ...

5

On the Variance of Antithetic Time Series

On the Variance of Antithetic Time Series

... antithetic time series to reduce fitted MSE has applications in statistical process control, and the analysis of engineering, scientific, medical and economic time ...in time series ...

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Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting.

Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting.

... Fuzzy time series was originally proposed by Song and Chissom [1] and it has been applied to forecast the enrollments at University of Alabama [2, ...fuzzy time series to forecast time ...

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The uncertainty of changepoints in time series

The uncertainty of changepoints in time series

... This thesis is motivated by three real datasets which exhibit different types of CPs. We firstly consider a dataset which is commonly featured in the CP lit- erature; Hamilton’s Gross National Product data (GNP, Hamilton ...

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