Linearity Testing Against a Fuzzy Rule-based Model

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.fuzzy rule-based models, time series, linearity test, statistical inference

Additional Smoothing Transition Autoregressive Models

In this paper we present, propose and examine additional membership functions. There is no reason why more functions cannot be proposed. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. Because Smoothing Transition Autoregressive (STAR) models follow fuzzy logic approach, more functions should be tested. Furthermore, fuzzy rules can be incorporated or other training or computational methods can be applied as the error backpropagation algorithm instead to nonlinear squares. Some numerical applications are presented and MATLAB routines are providedSmoothing transition; exponential, logistic; Gaussian, Generalized Bell function; tangent hyperbolic; stock returns; unemployment rate; forecast; MATLAB

Wavelet-Based Prediction for Governance, Diversification and Value Creation Variables

We study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure/diversification relationship. It consists of a dynamic procedure based on wavelets. A comparison with Neural Networks, the most used method, is provided to prove the efficiency of the here-developed one. The empirical tests are conducted on a set of French firms.Comment: 22 page

Defining and applying prediction performance metrics on a recurrent NARX time series model.

International audienceNonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output. Some prediction metrics are also proposed to assess the quality of predictions. This metrics enable to compare different prediction schemes and provide an objective way to measure how changes in training or prediction model (Neural network architecture) affect the quality of predictions. Results show that the proposed NARX approach consistently outperforms the prediction obtained by the RNN neural network

Fish scale remover machine

Laksa, sata and fish ball is a fish based food. Fish belong to groups of coldblooded animals. Fish lives in water, breathe through gills and use fins to move. According to the dictionary, scales are layered flaky skin on the surface of the fish skin. Scales function as a tool of defending them from their enemies. Hardness of fish scales are different depending on the types of fish being used for productio

Application of a Modified Generalized Regression Neural Networks Algorithm in Economics and Finance

In this paper we propose an alternative and modified Generalized Regression Neural Networks Autoregressive model (GRNN-AR) in S&P 500 and FTSE 100 index returns, as also in Gross domestic product growth rate of Italy, USA and UK. We compare the forecasts with Generalized Autoregressive conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models. The results indicate that GRNN outperform significant the conventional econometric models and can be an efficient alternative tool for forecasting. The MATLAB algorithm we propose is provided in appendix for further applications, suggestions, modifications and improvements

Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches

To predict a return characteristic, one may construct models of different complexity describing the dynamics of different objects. The most complex object is the entire predictive density, while the least complex is the characteristic whose forecast is of interest. This paper investigates, using experiments with real data, the relation between the complexity of the modeled object and the predictive quality of the return characteristic of interest, in the case when this characteristic is a return sign, or, equivalently, the direction-of-change. Importantly, we carry out the comparisons assuming that the underlying loss function is asymmetric, which is more plausible than the quadratic loss still prevailing in the analysis of returns. Our experiments are performed with returns of various frequencies on a stock market index and exchange rate. By and large, modeling the dynamics of returns by autoregressive conditional quantiles tends to produce forecasts of higher quality than modeling the whole predictive density or modeling the return indicators themselves.Directional prediction, sign prediction, model complexity, prediction quality, asymmetric loss, predictive density, conditional quantiles, binary autoregression