Abstract
Reliable and Accurate Prediction of Time Series over Large Future Horizons Has Become the New Frontier of the Forecasting Discipline. Current Approaches to Long-Term Time Series Forecasting Rely Either on Iterated Predictors, Direct Predictors Or, More Recently, on the Multi-Input Multi- Output (MIMO) Predictors. the Iterated Approach Suffers from the Accumulation of Errors, the Direct Strategy Makes a Conditional Independence Assumption, Which Does Not Necessarily Preserve the Stochastic Properties of the Time Series, While the MIMO Technique is Limited by the Reduced Flexibility of the Predictor. the Paper Compares the Direct and MIMO Strategies and Discusses their Respective Limitations to the Problem of Longterm Time Series Prediction. It Also Proposes a New Methodology that is a Sort of Intermediate Way between the Direct and the MIMO Technique. the Paper Presents the Results Obtained with the Estsp 2007 Competition Dataset. ©2009 IEEE.
Recommended Citation
S. B. Taieb et al., "Long-Term Prediction of Time Series by Combining Direct and Mimo Strategies," Proceedings of the International Joint Conference on Neural Networks, pp. 3054 - 3061, article no. 5178802, Institute of Electrical and Electronics Engineers, Nov 2009.
The definitive version is available at https://doi.org/10.1109/IJCNN.2009.5178802
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142443553-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
18 Nov 2009
