A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Yu, Lean; Wang, Shouyang; Lai, Kin Keung

doi:10.1016/j.cor.2004.06.024

Cited by 225 publications

(118 citation statements)

References 26 publications

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“…Generally speaking, the results obtained from the two tables also indicate that the prediction performance of the proposed neural network based fuzzy group forecasting model is better than those of the single neural network model, linear regression and logit regression forecasting models for the three main currencies. The main reasons are that (1) aggregating multiple predictions into a group consensus can definitely improve the performance, as Yu et al [1] revealed; (2) fuzzification of the predictions may generalize the model by processing some uncertainties of forecasting; and (3) as an "universal approximator", neural network might also make a contribution for the performance improvement.…”

Section: Three Foreign Exchange Rates Prediction Experimentsmentioning

confidence: 99%

“…We also take the data from January 2001 to November 2006 as out-of-sample (testing periods) data sets (71 observations), which is used to evaluate the good or bad performance of prediction based on some evaluation measurement. For evaluation, two typical indicators, normalized mean squared error (NMSE) [1] and directional statistics (D stat ) [1] are used. In addition, for comparison purpose, linear regression (LinR), logit regression (LogR) and single FNN model are used here.…”

Section: Three Foreign Exchange Rates Prediction Experimentsmentioning

confidence: 99%

“…Over this time, the research stream has gained momentum with the advancement of computer technologies, which have made many elaborate computation methods available and practical [1]. Due to its high volatility, foreign exchange rates forecasting is regarded as a rather challenging task.…”

Section: Introductionmentioning

confidence: 99%

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Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

Lai

Wang

2007

Computational Science – ICCS 2007

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Abstract. This study proposes a novel neural-network-based fuzzy group forecasting model for foreign exchange rates prediction. In the proposed model, some single neural network models are first used as predictors for foreign exchange rates prediction. Then these single prediction results produced by each single neural network models are fuzzified into some fuzzy prediction representations. Subsequently, these fuzzified prediction representations are aggregated into a fuzzy group consensus, i.e., aggregated fuzzy prediction representation. Finally, the aggregated prediction representation is defuzzified into a crisp value as the final prediction results. For illustration and testing purposes, a typical numerical example and three typical foreign exchange rates prediction experiments are presented. Experimental results reveal that the proposed model can significantly improve the prediction performance for foreign exchange rates.

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Section: Three Foreign Exchange Rates Prediction Experimentsmentioning

confidence: 99%

Section: Three Foreign Exchange Rates Prediction Experimentsmentioning

confidence: 99%

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Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

Lai

Wang

2007

Computational Science – ICCS 2007

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“…In this study, a standard FNN is employed to realize nonlinear mapping [9]. Actually the paper uses the k lag terms (y t-i , i = 1, 2, …, k), m related variables (x j , j = 1, 2, …, m), and p forecasts as another neural network inputs to construct the nonlinear ensemble model.…”

Section: Nonlinear Ensemble Forecasting Modelmentioning

confidence: 99%

A Hybrid Econometric-AI Ensemble Learning Model for Chinese Foreign Trade Prediction

Wang

Lai

2007

Computational Science – ICCS 2007

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Abstract. Due to the complexity of economic system, the interactive effects of economic variables or factors on Chinese foreign trade make the prediction of China's foreign trade extremely difficult. To analyze the relationship between economic variables and foreign trade, this study proposes a novel nonlinear ensemble learning methodology hybridizing nonlinear econometric model and artificial neural networks (ANN) for Chinese foreign trade prediction. In this proposed learning approach, an important econometrical model, the cointegration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of the economic variables on Chinese foreign trade from a multivariate analysis perspective. Then an ANN-based EC-VAR model is used to capture the nonlinear patterns hidden between foreign trade and economic factors. Subsequently, for introducing the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also incorporated into the nonlinear ANN-based EC-VAR model. Finally, all economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as another neural network inputs for ensemble prediction purpose. For illustration, the proposed ensemble learning methodology integrating econometric techniques and artificial intelligence (AI) methods is applied to Chinese export trade prediction problem.

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“…Torben [2] improved GARCH model on the limitations of the sample distribution to study the distribution of the daily exchange rate fluctuations of the German Mark and the Japanese Yen against the U.S. dollar. Yu et al, [3] proposed a novel nonlinear ensemble forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. Routa et al, [4] proposed a simple but promising hybrid prediction model by suitably combining an adaptive autoregressive moving average (ARMA) architecture and differential evolution (DE) based training of its feed-forward and feedback parameters.…”

Section: Introductionmentioning

confidence: 99%

Comparing RMB Exchange Rate Forecasting Accuracy based on Dynamic BP Neural Network Model and the ARMA Model

Ye¹,

Ren²

2016

J Stock Forex Trad

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A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates

Cited by 225 publications

References 26 publications

Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

Neural-Network-Based Fuzzy Group Forecasting with Application to Foreign Exchange Rates Prediction

A Hybrid Econometric-AI Ensemble Learning Model for Chinese Foreign Trade Prediction

Comparing RMB Exchange Rate Forecasting Accuracy based on Dynamic BP Neural Network Model and the ARMA Model

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