Neural network ensembles for time series prediction
Authors: Ruta, D. and Gabrys, B.
Journal: IEEE International Conference on Neural Networks - Conference Proceedings
Pages: 1204-1209
ISBN: 9781424413805
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2007.4371129
Abstract: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 generalisation ability achieved through a highly diversified set of complementary NN models. The model has been evaluated within NISIS Competition 2006 and NN3 Competition 2007 concerning prediction of univariate and multivariate time-series. It showed the best predictive performance among 12 competitive models in the NISIS 2006 and is under evaluation within NN3 2007 Competition. ©2007 IEEE.
https://eprints.bournemouth.ac.uk/8522/
Source: Scopus
Neural network ensembles for time series prediction
Authors: Ruta, D. and Gabrys, B.
Journal: 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6
Pages: 1204-1209
ISBN: 978-1-4244-1379-9
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2007.4371129
https://eprints.bournemouth.ac.uk/8522/
Source: Web of Science (Lite)
Neural Network Ensembles for Time Series Prediction
Authors: Ruta, D. and Gabrys, B.
Conference: Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Dates: 12-17 August 2007
Pages: 1204-1209
Publisher: IEEE Press
ISBN: 978-1-4244-1380-5
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2007.4371129
Abstract: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 generalisation ability achieved through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006 and NN3 Competition 2007 concerning prediction of univariate and multivariate time-series. It showed the best predictive performance among 12 competitive models in the NISIS 2006 and is under evaluation within NN3 2007 Competition.
https://eprints.bournemouth.ac.uk/8522/
Source: Manual
Preferred by: Dymitr Ruta
Neural Network Ensembles for Time Series Prediction.
Authors: Ruta, D. and Gabrys, B.
Journal: IJCNN
Pages: 1204-1209
Publisher: IEEE
ISBN: 978-1-4244-1379-9
https://eprints.bournemouth.ac.uk/8522/
https://ieeexplore.ieee.org/xpl/conhome/4370890/proceeding
Source: DBLP
Neural Network Ensembles for Time Series Prediction
Authors: Ruta, D. and Gabrys, B.
Conference: Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Pages: 1204-1209
Publisher: IEEE Press
ISBN: 978-1-4244-1380-5
ISSN: 1098-7576
Abstract: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 generalisation ability achieved through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006 and NN3 Competition 2007 concerning prediction of univariate and multivariate time-series. It showed the best predictive performance among 12 competitive models in the NISIS 2006 and is under evaluation within NN3 2007 Competition.
https://eprints.bournemouth.ac.uk/8522/
Source: BURO EPrints