Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a...

This study focuses on the question whether nonlinear transformation of lagged time series values and residuals are able to systematically improve the average forecasting performance of simple Autoregressive models. Furthermore it investigates the potential superior forecasting results of a...

Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate...

The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are introduced and their properties discussed. These include the smooth transition regression model, the switching regression model whose univariate counterpart is called threshold...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can...

The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002...

In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and...

The purpose of this paper is to present a neuro-fuzzy approach of financial distress pre-warning model appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) from 2002 through...

In this paper we present the neuro-fuzzy technology for the prediction of economic crisis of USA economy. Our findings support ANFIS models to traditional discrete choice models of Probit and Logit, indicating that the last models are not very useful for forecasting purposes. We have developed a...

In this paper discrete choice models, Logit and Probit are examined in order to predict the economic recession or expansion periods in USA. Additionally we propose an adaptive neurofuzzy inference system with triangular and Gaussian membership functions and genetic algorithms training...