State space modeling of time series
State space modeling of time series
Finite impulse response neural networks with applications in time series prediction
Finite impulse response neural networks with applications in time series prediction
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
The Theory of Discrete Lagrange Multipliers for Nonlinear Discrete Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Violation-guided learning for constrained formulations in neural-network time-series predictions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Neural Networks
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
A stock recommendation system exploiting rule discovery in stock databases
Information and Software Technology
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In this paper, we develop new constrained artificial-neural-network (ANN) formulations and learning algorithms to predict future stock prices, a difficult time-series prediction problem. Specifically, we characterize stock prices as a non-stationary noisy time series, identify its predictable low-frequency components, develop strategies to predict missing low-frequency information in the lag period of a filtered time series, model the prediction problem by a recurrent FIR ANN, formulate the training problem of the ANN as a constrained optimization problem, develop new constraints to incorporate the objectives in cross validation, solve the learning problem using algorithms based on the theory of Lagrange multipliers for nonlinear discrete constrained optimization, and illustrate our prediction results on three stock time series. There are two main contributions of this paper. First, we present a new approach to predict missing low-pass data in the lag period when low-pass filtering is applied on a time series. Such predictions allow learning to be carried out more accurately. Second, we propose new constraints on cross validation that can improve significantly the accuracy of learning in a constrained formulation. Our experimental results demonstrate good prediction accuracy in a 10-day horizon.