Tree-structured smooth transition regression models
Computational Statistics & Data Analysis
Equivalences between neural-autoregressive time series models and fuzzy systems
IEEE Transactions on Neural Networks
Testing for heteroskedasticity of the residuals in fuzzy rule-based models
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters
Applied Intelligence
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Financial time series forecasting with a bio-inspired fuzzy model
Expert Systems with Applications: An International Journal
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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-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.