Foreign-Exchange-Rate Forecasting With Artificial Neural Networks

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

doi:10.1007/978-0-387-71720-3

Cited by 57 publications

(33 citation statements)

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“…Thus, from some perspectives, the FNN model is equivalent to a nonlinear autoregressive (NAR) model (Yu et al, , 2007b. The main reason of selecting FNN as a predictor is that it is often viewed as a "universal approximator" (Hornik et al, 1989).…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Yu¹,

Wang²,

Lai³

2008

Energy Economics

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596

330

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Yu¹,

Wang²,

Lai³

2008

Energy Economics

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596

330

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“…Furthermore, to test the generalization and universality of the proposed learning method, the two daily crude oil price datasets with much larger scales are also introduced: The dataset D1 covers from January 04, 2010, to March 18, 2014, with a total of 1060 observations and the dataset D2 from January 03, 2006, to March 18, 2014, with a total of We treat the first 70 % of the data as training set, and the remaining 30 % as testing set for performance evaluation [73]. In this experiment, only one-step-ahead prediction is performed.…”

Section: Sensitivity Analysismentioning

confidence: 99%

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Dai

Tang

et al. 2015

Neural Comput & Applic

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In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

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“…But in crude oil price forecasting, improved decisions usually depend on correct forecasting of direction, of actual price, and predicted price, ( ) and �( ). The ability to predict movement direction can be measured by a directional statistic ( ) (Yu et al, 2007b(Yu et al, , 2008, which can be expressed as…”

Section: Research Data and Evaluation Criteriamentioning

confidence: 99%

“…Among AI models, artificial neural networks (ANNs) are often regarded as a class of reliable and cost-effective methods for crude oil price prediction. The neural network model, particularly the multi-layer feed-forward neural network (FNN), can be trained to approximate any smooth and measurable nonlinear function without prior assumptions on the original data (Yu et al, 2007b;Lee & Lee, 2016); it has produced many promising results in this field (Kaboudan, 2001;Mirmirani and Li, 2004;Wang et al, 2004Wang et al, , 2005Shambora and Rossiter, 2007;Yu et al, 2007a, Chou, 2016. These studies have shown that ANN models are very effective in simulating and describing the dynamics of non-stationary time series due to its unique non-parametric, noise-tolerant and highly adaptive characteristics.…”

Section: Introductionmentioning

confidence: 99%

Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Zhang

Wang

2017

EURASIA J MATH SCI T

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Crude oil price forecasting is one of the most important topics in the field of energy research. Accordingly, numerous methods such as statistical, econometrical and intelligent approaches are applied for crude oil price forecasting. In this paper, a typical competitive learning algorithm, support vector machine (SVM), is empirically investigated to verify the feasibility and potentiality of SVM in crude oil price forecasting. For this purpose, five different prediction models, feed-forward neural networks (FNN), auto-regressive integrated moving average (ARIMA) model, fractional integrated ARIMA (ARFIMA) model, Markov-switching ARFIMA (MS-ARFIMA) model, and random walk (RW) model are used in the study. Experimental results obtained show that the SVM model outperforms the other five methods, implying that it is a fairly good candidate for crude oil price forecasting in terms of either one-step prediction or multi-step prediction.

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Foreign-Exchange-Rate Forecasting With Artificial Neural Networks

Cited by 57 publications

References 0 publications

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

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