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We present a Bayesian surrogate model for the analysis of periodic or quasi-periodic time series data. We describe a computationally efficient implementation that enables Bayesian model comparison. We...
A wavelet network is an important tool for analyzing time series especially when it is nonlinear and non-stationary. It takes advantage of high resolution of wavelets and learning and feed forward nat...
Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. Dur...
Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute...
A detailed nonlinear time series analysis has been made of two daytime geomagnetic pulsation events being recorded at L'Aquila (Italy, L ≈ 1.6) and Niemegk (Germany, L ≈ 2.3). Grassberger and Procacci...
An analysis of time series of monthly mean temperatures ranging from 1895 to 1989 is performed through application of Singular Spectrum Analysis (SSA) to data of several places in the USA. A common dy...
We analyze the monthly 1866-2000 Southern Oscillation Index (SOI) data to determine: 1) whether the SOI data are sufficiently noise-free that useful predictions can be made from them, and 2) in part...
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...
The equivalent width and central depth data of Mn 539.4 nm solar spectral line, observed in the period from 1979 to 1992 at Kitt Peak Observatory, was analyzed in pursuit for periodic changes. As th...
I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and...

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