Neural Networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex realworld sensor data, Artificial Neural Networks (ANNs) are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their imp...

Prediction and classification of particular faults in rotating machinery, based on a given set of measurements, could significantly reduce the overall costs of maintenance and repair. Usually the vibration signal is sampled with a very high frequency due to its nature, thus it is quite difficult to do considerably long forecasting based on the methods, which are suitable for e.g. financial time...

Traditionally, financial crisis EarlyWarning Systems (EWSs) have relied onmacroeconomic leading indicatorswhen forecasting the occurrence of such events. This paper extends such discrete-choice EWSs by taking the persistence of the crisis phenomenon into account. The dynamic logit EWS is estimated using an exact maximum likelihood estimation method in both a country-by-country and a panel frame...

Improving out-of-sample forecasting is one of the main issues in financial research. Previous studies have achieved this objective by increasing the number of input variables or changing the kind of input variables. Changing the forecasting model is another possible approach to improve out-of-sample forecasting. Most researches have focused on linear models, while few have studied nonlinear mod...

The purpose of this paper is present probabilistic neural networks (PNN) as an alternative quantitative technique to both linear discriminant analysis (LDA) and backpropagated neural networks (BPNN) for forecasting corporate solvency. Although traditionally this task has been approached with rather simpler linear techniques such as LDA, there is increasing empirical evidence of the superiority ...

The purpose of this paper is to consider how to forecast implied volatility for a selection of UK companies with traded options on their stocks. We consider a range of GARCH and logARFIMA based models as well as some simple forecasting rules. Overall, we find that a logARFIMA model forecasts best over short and long horizons. Key-words : Implied Volatility, Forecasting, ARFIMA, GARCH, log-ARFIM...

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods...

This paper presents a novel trend-based segmentation method TBSM and the support vector regression SVR for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation PLR , has been applied to locate a set of tradi...

Locally stationary wavelet (LSW) processes, built on non-decimated wavelets, can be used to analyze and forecast non-stationary time series. They have been proved useful in the analysis of financial data. In this paper we first carry out a sensitivity analysis, then propose some practical guidelines for choosing the wavelet bases for these processes. The existing forecasting algorithm is found ...

Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional correlations processes, although with the drawback, when the number of financial returns series considered increases, that the parameterizations entail too many parameters.In general, the interaction between model parametrization of the second conditional moment and the conditional density of ...