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In this study we have used dynamical characteristies such as Lyapunov exponents, nonlinear dynamic models and mutual information for the nonlinear analysis of the magnetospheric AE index time series. ...
A long AE index time series is used as a crucial magnetospheric quantity in order to study the underlying dynainics. For this purpose we utilize methods of nonlinear and chaotic analysis of time serie...
The singular value decomposition (SVD) analysis is used at different stages in this paper in order to extract useful information concerning the underlying dynamics of the magnetospheric AE index. As a...
This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the co...
Empirical determination of the scaling properties and exponents of time series presents a formidable challenge in testing, and developing, a theoretical understanding of turbulence and other out-of-eq...
We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the res...
The paper presents an approach to estimate parameters of a local stationary AR(1) time series model by maximization of a local likelihood function. The method is based on a propagation-separation proc...
Methodological Aspects on River Cryophenology Exemplified by a Tricentennial Break-up Time Series from Tornio.
The statistical tests used for the detection and adjustment of homogeneity breaks in climatic time series are mostly objective. However, subjective decisions on the design of the testing procedure are...

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