Winning Entry of the K. U. Leuven Time Series Prediction Competition

The K.U.Leuven competition data: a challenge for advanced neural network techniques

In this paper we shortly discuss the K.U. Leuven time-series prediction competition, which has been held in the framework of the International Workshop on Advanced Black-Box Techniques for Nonlinear Modeling, K.U.Leuven Belgium July 8-1

Efficient Hybrid Neural Network for Chaotic Time Series Prediction

We propose an efficient hybrid neural network for chaotic time series prediction. The hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropagation and a local model, which is implemented as a time delay embedding. The feed-forward network performs as the global approximation and the local model works as the local approximation. Experi...

CATS benchmark time series prediction by Kalman smoother with cross-validated noise density

This article presents the winning solution to the CATS time series prediction competition. The solution is based on classical optimal linear estimation theory. The proposed method models the long and short term dynamics of the time series as stochastic linear models. The computation is based on a Kalman smoother, in which the noise densities are estimated by cross-validation. In time series pre...

Observation of Stacking Fault Tetrahedral in TWIP Steel

Low stacking fault energy face centered cubic (FCC) materials contain characteristic defect structures. Stacking fault tetrahedral are one of those rare structures that occur under special experimental conditions. For the first time, stacking fault tetrahedral were observed in Fe-30Mn-3Al-3Si twinning induced plasticity (TWIP) steel. Their presence resulted from a quenching heat treatment.

Membrane Research in a Global Context Requires Collaboration, not Competition!