There are many parameters which are very difficult to calibrate in the threshold autoregressive prediction model for nonlinear time series. The threshold value, autoregressive coefficients, and the delay time are key parameters in the threshold autoregressive prediction model. To improve prediction precision and reduce the uncertainties in the determination of the above parameters, a new DNA de...

Keywords: Ensemble pruning Time series prediction Rank-based ensemble pruning Complementarity measure for time series prediction (ComTSP) Concurrency thinning for time series prediction (ConTSP) Reduce Error pruning for time series prediction (ReTSP-Trend) Time window size a b s t r a c t Ensemble pruning is a desirable and popular method to overcome the deficiency of high computational costs o...

There exists a wide range of paradigms, and a high number of different methodologies that are applied to the problem of time series prediction. Most of them are presented as a modified function approximation problem using input/output data, in which the input data are expanded using values of the series at previous steps. Thus, the model obtained normally predicts the value of the series at a t...

Prediction models based on different concepts have been proposed in recent years. Improving the accuracy of prediction models has remained as a challenging task for researchers. The prediction accuracy depends not only on the model but also on the complexity of the data. Hence, it is important to choose the best model based on the complexity of data in the prediction. The time series prediction...

This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nearest Neighbors approximator is used to improve the input selection and to provide a simple but accurate prediction method. Due to its simplicity the method is repeated to build a large number of models that are used f...

This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Taken...

Time series forecasting is of fundamental importance for a variety of domains including the prediction of earthquakes, financial market prediction, and the prediction of epileptic seizures. We present an original approach that brings a novel perspective to the field of long-term time series forecasting. Nonlinear properties of a time series are evaluated and used for long-term predictions. We u...

Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of nonlinear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction an...

Deterministic nonlinear prediction is a pow erful tec hnique for the analysis and prediction of time series generated by nonlinear dynamical systems. In this paper the use of a Kohonen netw ork asa component of one deterministic nonlinear prediction algorithm is suggested. In order to evaluate the performance of the proposed algorithm, it was applied to the prediction of time series generated b...