Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
The Racing Algorithm: Model Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Model selection in neural networks
Neural Networks
Lazy learning meets the recursive least squares algorithm
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Local Learning for Iterated Time-Series Prediction
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On Learning Vector-Valued Functions
Neural Computation
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Methodology for long-term prediction of time series
Neurocomputing
Adaptive mixtures of local experts
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Finding the embedding dimension and variable dependencies in time series
Neural Computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Long-term prediction of time series by combining direct and MIMO strategies
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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
Multi-output nonparametric regression
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
The multi-agent system for prediction of financial time series
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
IEEE Transactions on Neural Networks
A comparison between neural-network forecasting techniques-case study: river flow forecasting
IEEE Transactions on Neural Networks
Long-term time series prediction using OP-ELM
Neural Networks
Neural network ensemble operators for time series forecasting
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for multi-step ahead forecasting and comparing them in theoretical and practical terms. To attain such an objective, we performed a large scale comparison of these different strategies using a large experimental benchmark (namely the 111 series from the NN5 forecasting competition). In addition, we considered the effects of deseasonalization, input variable selection, and forecast combination on these strategies and on multi-step ahead forecasting at large. The following three findings appear to be consistently supported by the experimental results: Multiple-Output strategies are the best performing approaches, deseasonalization leads to uniformly improved forecast accuracy, and input selection is more effective when performed in conjunction with deseasonalization.