Journal Article10.1016/S0925-2312(03)00437-5
Neural network protocols and model performance
Teo Jasic,Douglas Wood +1 more
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TL;DR: This paper drops a number of customary features that are used in neural modeling to improve performance yet are able to predict daily returns for the four main exchange rates with accuracy above levels previously reported for comparable data sets.
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About: This article is published in Neurocomputing. The article was published on 01 Oct 2003. The article focuses on the topics: Artificial neural network.
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Citations
Optimal Forecast Combination Based on Neural Networks for Time Series Forecasting
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A new model selection strategy in time series forecasting with artificial neural networks
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TL;DR: A new model selection strategy (IHTS) for forecasting with neural networks is proposed that improves the performance of the neural networks statistically as compared with the classic model selection method in the simulated and real data sets.
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Stock Market Prediction using Feed-forward Artificial Neural Network
TL;DR: The proposed model succeeded in prediction of the trends of stock market with 100% prediction accuracy and is suggested about training algorithms and training parameters that must be chosen in order to fit time series kind of complicated data to a neural network model.
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Are foreign exchange rates predictable? a survey from artificial neural networks perspective *
Lean Yu,Shouyang Wang,Wei Huang,Kin Keung Lai +3 more
- 01 Jan 2007
TL;DR: In this article, the authors present a survey on the applications of artificial neural networks (ANNs) in foreign exchange rates forecasting, and compare the performances of ANNs and those of other forecasting methods, finding mixed results.
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A hybrid systematic design for multiobjective market problems: a case study in crude oil markets
TL;DR: An application of hybrid systematic design using the hybrid of intelligent systems, particularly fuzzy rule base and neural networks can guide the decision maker towards noninferior solutions in multiobjective market problems.
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