Clustering time series is usually limited by the fact that the length of the time series has a significantly negative influence on the runtime. On the other hand, approximative c...
In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection...
Antti Sorjamaa, Jin Hao, Nima Reyhani, Yongnan Ji,...
This paper proposes a simple methodology to construct an iterative neural network which mimics a given chaotic time series. The methodology uses the Gamma test to identify a suita...
Antonia J. Jones, Steve Margetts, Peter Durrant, A...
Usually time series prediction is done with regularly sampled data. In practice, however, the data available may be irregularly sampled. In this case the conventional prediction me...
Abstract. One of the main problems associated with arti cial neural networks online learning methods is the estimation of model order. In this paper, we report about a new approach...