A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting

In recent years, neural networks is increasingly adopted in the prediction of exchange rate. However, most of them predict a specific number, which can not help the speculators too much because small gap between the predicted values and the actual values will lead to disastrous consequences. In our study, our purpose is to present a model to forecast the fluctuation range of the exchange rate by combining Fuzzy Granulation with Continuous-valued Deep Belief Networks (CDBN), and the concept of "Stop Loss" is introduced for making the environment of our profit strategy close to the real foreign exchange trade market. The proposed model is applied to forecasting both Euro/US dollar and British pound/US dollar exchange rate in our experiments. Experimental results show that the proposed method is more profitable in the trading process than other typical models.

[1]  Yiyu Yao,et al.  MGRS: A multi-granulation rough set , 2010, Inf. Sci..

[2]  W. Pedrycz,et al.  Distributed Intervals: A Formal Framework for Information Granulation , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[3]  Witold Pedrycz,et al.  Feature analysis through information granulation and fuzzy sets , 2002, Pattern Recognit..

[4]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[5]  Ming Zhang,et al.  Application of Higher-Order Neural Networks to Financial Time-Series Prediction , 2006 .

[6]  Garth P. McCormick,et al.  Communications to the Editor--Exponential Forecasting: Some New Variations , 1969 .

[7]  Shen Furao,et al.  Forecasting exchange rate with deep belief networks , 2011, The 2011 International Joint Conference on Neural Networks.

[8]  Hooman Tahayori,et al.  Email Granulation Based On Augmented Interval Type-2 Fuzzy Set Methodologies , 2007, GrC.

[9]  Walter Enders,et al.  ARIMA and Cointegration Tests of PPP under Fixed and Flexible Exchange Rate Regimes , 1988 .

[10]  Sankar K. Pal,et al.  Rough-wavelet granular space and classification of multispectral remote sensing image , 2011, Appl. Soft Comput..

[11]  Y. Yao,et al.  Information Granulation for Web based Information Retrieval Support Systems , 2003 .

[12]  Alan F. Murray,et al.  Continuous restricted Boltzmann machine with an implementable training algorithm , 2003 .

[13]  Jiye Liang,et al.  A new measure of uncertainty based on knowledge granulation for rough sets , 2009, Inf. Sci..

[14]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[15]  Witold Pedrycz,et al.  Computational Intelligence: An Introduction , 1997, Computational Intelligence and Quantitative Software Engineering.

[16]  Alan F. Murray,et al.  Novelty detection using products of simple experts--a potential architecture for embedded systems , 2001, Neural Networks.

[17]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[18]  Yan Shi,et al.  Developing fast predictors for large-scale time series using fuzzy granular support vector machines , 2013, Appl. Soft Comput..

[19]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[20]  F. Diebold,et al.  The dynamics of exchange rate volatility: a multivariate latent factor ARCH model , 1986 .

[21]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[22]  J. Muth.  Optimal Properties of Exponentially Weighted Forecasts , 1960 .

[23]  Witold Pedrycz,et al.  Use of a fuzzy granulation-degranulation criterion for assessing cluster validity , 2011, Fuzzy Sets Syst..

[24]  Khurshid M. Kiani,et al.  Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures , 2008 .

[25]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[26]  Chung-Ming Kuan,et al.  Forecasting exchange rates using feedforward and recurrent neural networks , 1992 .

[27]  Qinghua Hu,et al.  Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation , 2007, Pattern Recognit..

[28]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[29]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[30]  Corrado Mencar,et al.  Interpretability of Fuzzy Information Granules , 2009, Human-Centric Information Processing Through Granular Modelling.