System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
A simulation study of artificial neural networks for nonlinear time-series forecasting
Computers and Operations Research
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Recursive Lazy Learning for Modeling and Control
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Methodology for long-term prediction of time series
Neurocomputing
A review of feature selection techniques in bioinformatics
Bioinformatics
Minimising the delta test for variable selection in regression problems
International Journal of High Performance Systems Architecture
Multiresponse sparse regression with application to multidimensional scaling
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Editorial: European Symposium on Times Series Prediction
Neurocomputing
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Long-term time series prediction is a difficult task. This is due to accumulation of errors and inherent uncertainties of a long-term prediction, which leads to deteriorated estimates of the future instances. In order to make accurate predictions, this paper presents a methodology that uses input processing before building the model. Input processing is a necessary step due to the curse of dimensionality, where the aim is to reduce the number of input variables or features. In the paper, we consider the combination of the delta test and the genetic algorithm to obtain two aspects of reduction: scaling and projection. After input processing, two fast models are used to make the predictions: optimally pruned extreme learning machine and optimally pruned k-nearest neighbors. Both models have fast training times, which makes them suitable choice for direct strategy for long-term prediction. The methodology is tested on three different data sets: two time series competition data sets and one financial data set.