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A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting
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Outgoing claims: the papers this work cites, builds on, or argues with.
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The dynamics of exchange rate volatility: A multivariate latent factor ARCH model
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Forecasting exchange rates using feedforward and recurrent neural networks
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Computational Intelligence: An Introduction
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Approximation by superpositions of a sigmoidal function
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Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures
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Multilayer feedforward networks are universal approximators
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Rough-wavelet granular space and classification of multispectral remote sensing image
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Developing fast predictors for large-scale time series using fuzzy granular support vector machines
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Use of a fuzzy granulation–degranulation criterion for assessing cluster validity
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A new measure of uncertainty based on knowledge granulation for rough sets
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MGRS: A multi-granulation rough set
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Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation
inferredcites →https://doi.org/10.1016/s0031-3203(01)00102-9openalex-snapshot
Feature analysis through information granulation and fuzzy sets
inferredcites →https://doi.org/10.1016/s0165-0114(97)00077-8openalex-snapshot
Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
inferredcites →https://doi.org/10.1016/s0893-6080(01)00097-1openalex-snapshot
Novelty detection using products of simple experts—a potential architecture for embedded systems
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Continuous restricted Boltzmann machine with an implementable training algorithm
inferredcites →https://doi.org/10.1080/01621459.1960.10482064openalex-snapshot
Optimal Properties of Exponentially Weighted Forecasts
inferredcites →https://doi.org/10.1109/ccece.2007.354openalex-snapshot
Distributed Intervals: A Formal Framework for Information Granulation
inferredcites →https://doi.org/10.1109/grc.2007.58openalex-snapshot
Algorithms for Different Approximations in Incomplete Information Systems with Maximal Compatible Classes as Primitive Granules
inferredcites →https://doi.org/10.1109/icnn.1988.23959openalex-snapshot
Economic prediction using neural networks: the case of IBM daily stock returns
inferredcites →https://doi.org/10.1109/ijcnn.2011.6033368openalex-snapshot
Forecasting exchange rate with deep belief networks
inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-snapshot
A Fast Learning Algorithm for Deep Belief Nets
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Communications to the Editor—Exponential Forecasting: Some New Variations
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Arima and Cointegration Tests of PPP under Fixed and Flexible Exchange Rate Regimes
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Application of Higher-Order Neural Networks to Financial Time-Series Prediction
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Multilayer feedforward networks are universal approximators
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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