Technical Note: \cal Q-Learning
Machine Learning
Contender's network, a new competitive-learning scheme
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data equalisation with evidence combination for pattern recognition
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
Neural network learning using entropy cycle
Knowledge and Information Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Time Series Prediction and Neural Networks
Journal of Intelligent and Robotic Systems
A new evolutionary method for time series forecasting
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
A method for segmentation of switching dynamic modes in time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
WSEAS Transactions on Information Science and Applications
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Concurrency and Computation: Practice & Experience
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Time series prediction is traditionally handled by linear models such as autoregressive and moving-average. However they are unable to adequately deal with the non-linearity in the data. Neural networks are non-linear models that are suitable to handle the non-linearity in time series. When designing a neural network for prediction, two critical factors that affect the performance of the neural network predictor should be considered; they are namely: (1) the input dimension, and (2) the time delay. The former is the number of delayed values for prediction, while the latter is the time interval between two data. Prediction accuracy can be improved using suitable input dimension and time delay. A novel method, called reinforcement learning-based dimension and delay estimator (RLDDE), is proposed in this paper to simultaneously determine the input dimension and time delay. RLDDE is a meta-learner that tries to learn the selection policy of the dimension and delay under different distribution of the data. Two benchmarked datasets with different noise levels and one stock price are used to show the effectiveness of the proposed RLDDE together with the benchmarking against other methods.