This paper considers a linear model with time varying parameters controlled by a neural network to analyze and forecast nonlinear time series.We show that this formulation, called neural coefficient smooth transition autoregressive (NCSTAR) model, is in close relation to the threshold autoregressive (TAR) model and the smooth transition autoregressive (STAR) model with the advantage of naturall...

In this work, we propose a novel method to model time-varying autoregressive impulsive signals, which possess skewed or symmetric Alpha Stable distributions. The main contribution of this work is its ability to model both the unknown autoregressive coefficients and the distribution parameters, where all of them are time-varying. This method is a generalization of previous work by the same autho...

We present the algebraic representation and basic algorithms of MultiAspect Graphs (MAGs), a structure capable of representing multilayer and time-varying networks while also having the property of being isomorphic to a directed graph. In particular, we show that, as a consequence of the properties associated with the MAG structure, a MAG can be represented in matrix form. Moreover, we also sho...

In this study, it is clarified that dynamical system of roll motion can be approximated by time varying autoregressive models which is a kind of statistical model firstly. As the result, when it is possible to measure the time series of roll motion, the ship stability can be judged based on time series analysis by using the time varying autoregressive modeling procedure. As to the verification ...

In this paper, an original Markov-switching autoregressive model is proposed to describe the space-time evolution of wind fields. At first, a non-observable process is introduced in order to model the motion of the meteorological structures. Then, conditionally to this process, the evolution of the wind fields is described by using autoregressive models whith time varying coefficients. The prop...

A nonlinear generalization of the family of autoregressive signal models is introduced. This generalization can be viewed as an autoregressive model with state-varying parameters. For such signals, minimum mean-square error prediction can be reformulated as an interpolation problem. A novel interpretation of the signal as a codebook for its own prediction leads to an interpolation strategy rese...

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the Self-Exci...