Artificial Intelligence Review - Special issue on lazy learning
Lazy learning meets the recursive least squares algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Local Learning for Iterated Time-Series Prediction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Methodology for long-term prediction of time series
Neurocomputing
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Long-term prediction of time series by combining direct and MIMO strategies
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Multistep-Ahead time series prediction
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Direct and recursive prediction of time series using mutual information selection
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
HIS'12 Proceedings of the First international conference on Health Information Science
Fast variable selection for memetracker phrases time series prediction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Dynamical genetic programming in xcsf
Evolutionary Computation
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Accurate prediction of time series over long future horizons is the new frontier of forecasting. Conventional approaches to long-term time series forecasting rely either on iterated one-step-ahead predictors or direct predictors. In spite of their diversity, iterated and direct techniques for multi-step-ahead forecasting share a common feature, i.e. they model from data a multiple-input single-output mapping. In previous works, the authors presented an original multiple-output approach to multi-step-ahead prediction. The goal is to improve accuracy by preserving in the forecasted sequence the stochastic properties of the training series. This is not guaranteed for instance in direct approaches where predictions for different horizons are performed independently. This paper presents a review of single-output vs. multiple-output approaches for prediction and goes a step forward with respect to the previous authors contributions by (i) extending the multiple-output approach with a query-based criterion and (ii) presenting an assessment of single-output and multiple-output methods on the NN3 competition datasets. In particular, the experimental section shows that multiple-output approaches represent a competitive choice for tackling long-term forecasting tasks.