—Building a time series forecasting model by independent component analysis mechanism presents in the paper. Different from using the time series directly with the traditional A...
In this paper, we propose a generic non-linear approach for time series forecasting. The main feature of this approach is the use of a simple statistical forecasting in small regio...
Amaury Lendasse, Michel Verleysen, Eric de Bodt, M...
Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. An approach to this...
Adriano L. I. Oliveira, Fernando Buarque de Lima N...
Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many time series forecasti...
In this work, we propose to use the Zoomed-Ranking approach to ranking and selecting Artificial Neural Network (ANN) models for time series forecasting. Given a time series to fo...
Abstract. This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes techniques as a complementary step to the model....
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one ...