Neural Network Ensembles for Time Series Prediction

— Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex non-linear relationships between multidimensional features and the time series outputs. In order to exploit these time-disparate relationships for the improved time series forecasting, the system requires a flexible methodology of combining multiple prediction models applied to multiple versions of the temporal data under significant noise component and variable temporal depth of predictions. In reply to this challenge a composite time series prediction model is proposed which combines the strength of multiple neural network (NN) regressors applied to the temporally varied feature subsets and the postprocessing smoothing of outputs developed to further reduce noise. The key strength of the model is its excellent adaptability and gene...

• Type: Conference
• Year: 2007
• Where: IJCNN
• Authors: Dymitr Ruta, Bogdan Gabrys
• Keywords: IJCNN 2007, Prediction Model, Time Series, Time Series Outputs