This study presents two interval-valued time series approaches to construct multivariate multi-step ahead joint forecast regions based on bootstrap algorithms. The first approach is fitting a dynamic bivariate system via VAR process for minimum and maximum of the interval while second applies mid-points half-ranges series. As novel perspective, we adopt techniques into proposed obtain lower/upp...

There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling...

Reliable and precise multi-step-ahead tool wear state prediction is significant to modern industries for maintaining part quality reducing cost. This study proposes a Clustering Feature-based Recurrent Fuzzy Neural Network (CFRFNN) monitoring remaining useful life (RUL) based on K-means Clustering, (RFNN) Genetic Algorithm (GA). method utilized realize definition input signal division, which re...

In this paper, we propose some improvements for the problem of time series prediction with neural networks where a medium-term prediction horizon is needed. In particular, the ionospheric prediction service of the french Centre National d' Etudes des T el ecommunica-tions needs a six-month ahead prediction of a sunspots related time series which has a strong innuence on wave propagation in iono...

When dealing with time series the one step ahead forecasting problem based on experimental data is the problem of estimating the autoregression function of the underlying process When minimizing the expected forecast ing error is the main goal the exible approach has to be used to be able to adjust the complexity of the model to the complexity of the data Multilay ered perceptrons are a popular...

This paper introduces a novel system identification and tracking method for PieceWise Smooth (PWS) nonlinear stochastic hybrid systems. We are able to correctly identify and track challenging problems with diverse dynamics and low dimensional transitions. We exploit the composite structure system to learn a simpler model on each component/mode. We use Gaussian Process Regression techniques to l...