Non linear time series
Non linearities in macroeconomics : evaluation of non linear time series models
... 2 Non-linear time series models have been applied to characterise, for example: i business cycle asymmetries Hamilton, 1989; Beaudry and Koop, 1993; Potter, 1995; ii asymmetries in the e[r] ...
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Recursive estimation of non-linear time series models
... A r.ecursive scheme for simultaneous optimal estimation of conditional mean and variance in a nonlinear ARCH (autoregressive con- ditional heteroscedastic) model is also proposed.. Keywo[r] ...
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Non-Linear Time Series Modelling with Applications to Equity and Fixed Income Markets
... In the first chapter, which is a joint work with my supervisor Lars Stentoft, we examine the steady state properties of the TVAR model. Assuming the trigger variable is exoge- nous and the regime process follows a ...
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The association between ambient temperature and preterm birth in Shenzhen, China: a distributed lag non-linear time series analysis
... non-linear. The figure represented different patterns of temperature effect on the risk of PTB depending on the modification indicator used. The RR estimates of the PTB for decreasing temperatures values ...
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Study the Trend Pattern in COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm
... COVID-19 series in various countries is not linear because there are many reasons such as lockdown, infection modes, poor health infrastructure that control or expand this disease in the ...a ...
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Testing for Non Linear Dependence in Univariate Time Series An Empirical Investigation of the Austrian Unemployment Rate
... than linear time series models must be ...are non-linearly dependent, then non-linear time series models must be ...Several non-linear identification ...
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Automated classification of Persistent Scatterers Interferometry time series
... (PSI) time se- ries based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, ...
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Estimation for vector linear time series models
... Gaussian likelihood (or spectral equivalents to this likelihood) although Gaussianity is not required for any of the results to follow. Chapter 1 gives a brief introduction to the theory of multiple time ...
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Characterisation of linear predictability and non stationarity of subcutaneous glucose profiles
... strong non-stationarity, which limits the application of correlation-spectral ...of linear predictability by calculating the autocorrelation function of time series increments and applied ...
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A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets
... but non random in nature making it possible to identify the patterns based on the historical data and ...of time which can be broadly classified into two types of models that are mainly used for Electricity ...
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On modelling insights for emerging engineering problems : a case study on the impact of climate uncertainty on the operational performance of offshore wind farms
... a time series Monte Carlo simulation to model the performance of offshore wind farms, identifying non-linear relationships between climate, availability, energy output and capacity ...
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A diagnostic for the general linear model : an application to Time Series
... in time series where the correlation parameters have to be estimated the prediction er ror decomposition is ...complex time series to be expressed in a more manageable ...by ...
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Trend assessment: applications for hydrology and climate research
... The time series can be reconstructed by a linear combination of wavelets, anal- ogous to a reconstruction by sinusoids in Fourier ...essentially non-zero only within a finite in- terval of ...
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Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning
... novel non-linear method, the Recurrence Period Density Prototype Distance (RPD-PD), with stochastic recurrence periods derived from time- delay ...
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Non Gaussian structural time series models
... Taylor series expansion about the mean of Tjt , ...at time t-1, a t-l- Shephard's model this parameter is data independent, so that, eventually, this will settle to a constant ...NBD series of 700 ...
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Measuring the degree of non-stationarity of a time series
... dynamic linear model that can flexibly adapt to the underlying process structure, which we review ...dynamic linear models but could also be applied to other moments; local wavelet, S j ...
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Non-parametric regression with a latent time series
... local linear regression paradigm because of its many advantages, Fan and Gijbels ...latent time series, which is perhaps the main contribution of this ...
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Site-Specific Inelastic Displacement Amplification Factors
... of time records and are based on full hysteresis ...a series of non-linear single degree of freedom analyses using a suite of time histories established to meet site-specific spectra ...
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Estimation and identification for vector linear time series models
... recommending the class of recursive estimators given in the thesis. As mentioned in Remark 2.1(i) the non-recursive analogues of these estimators have desirable statistical and computational properties. ...
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A new approach for analysis of heart rate variability and QT variability in long-term ECG recording
... the non-stationarity challenge, it is possible to truncate the time series into overlapping or non-overlapping segments, and then evaluate the spectral ...the time series are ...
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