Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

Fathian, Mohammad; Kia, Arash Negahdari

doi:10.5267/j.msl.2011.12.008

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…The base of the pyramid is the original data of high resolution, and the top is a low-resolution approximation, and the size and resolution will be reduced as the pyramid upper moves. Many studies establish a prediction model via multiresolution analysis method to forecast gold price volatility [24][25][26]. This paper will use wavelet-based multiresolution analysis to explore memory feature in gold price volatility in different time scales.…”

Section: Reconstructionmentioning

confidence: 99%

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Zheng

Liu

Zhou

2018

Discrete Dynamics in Nature and Society

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Memory in finance is the foundation of a well-established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving -statistic on the basis of a modified / method to determine whether the time series has a long-range dependence and subsequently to apply wavelet-based multiresolution analysis to study the multifractality of the financial time series to determine the initial data windows. Finally, we check the moving -statistic estimation in wavelet analysis in the same condition; the paper selects the volatilities of the gold foreign exchange rates to evaluate the moving -statistic. According to the results, the method of testing memory established in this paper can identify the breakpoint of the memories effectively. Furthermore, this method can provide support for forecasting returns in the financial market.

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Section: Reconstructionmentioning

confidence: 99%

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Zheng

Liu

Zhou

2018

Discrete Dynamics in Nature and Society

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“…For instance, recurrent NN , ridgelet NN , hybrid NN , wavelet NN , hybrid network with Bayesian clustering , and adaptive neuro‐fuzzy inference system have been proposed for WPF. Moreover, prediction of irradiation and electrical energy production in PVs by multilayer perceptron (MLP) NN , dynamic NN , and evolutionary ANN have been presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Net demand prediction for power systems by a new neural network‐based forecasting engine

Abedinia

Amjady

2016

Complexity

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Integration of renewable generations, such as wind and photovoltaic, into electrical power systems is rapidly growing throughout the world. Stochastic and variable nature of these resources makes some operational challenges to power systems. The most effective way to tackle these challenges is short‐term prediction of their available powers. Despite various developed methods to forecast generation of renewable resources, still they have large errors, which may lead to under/over‐commitment of conventional generators in power systems. Prediction of net demand (ND), defined as electrical load minus renewable generations, can provide useful information for accurate scheduling of conventional generators. In this article, characteristics of the time series of electric load, renewable generations and ND are analyzed, and a new hybrid prediction strategy is presented for direct prediction of ND. The training mechanism of the proposed forecasting engine is composed of a new stochastic search method and Levenberg–Marquardt learning algorithm based on an iterative procedure and greedy search. The suggested prediction strategy is tested on different real‐world power systems and its obtained results are compared with the results of several other forecast methods and published literature figures. These comparisons confirm the validity of the developed forecasting strategy. © 2016 Wiley Periodicals, Inc. Complexity 21: 296–308, 2016

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Gold Price Forecasting Using Machine Learning Techniques: Review of a Decade

Das

Nayak

Rao

et al. 2021

Computational Intelligence in Pattern Recognition

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Price of the gold plays a major role in monetary as well as financial systems. Prediction and forecasting the upcoming tendency of gold prices and other valuable metals will be helpful for investors and money managers to evade choosing when to supply this commodity. Central banks throughout the globe uphold gold reserves to assure the currency holders, the money of their shareholders, and foreigndebt creditors. They also utilize the gold treasury as a means to manage inflation and toughen their country's economic standing. During this procedure, the prediction of the gold rate has become the biggest issue now a days. So, various methods, especially intelligent techniques, have played a vital role in predicting gold prices. Moreover, a comparative investigation on the impact of machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), linear regression (LR), decision tree (DT), and other hybrid methods for gold price forecasting has been made. Some significant research directions for additional research on gold price prediction are highlighted which may assist the researchers to widen proficient intelligent techniques for the prediction of gold rate.

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Exchange rate prediction with multilayer perceptron neural network using gold price as external factor

Cited by 11 publications

References 28 publications

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Net demand prediction for power systems by a new neural network‐based forecasting engine

Gold Price Forecasting Using Machine Learning Techniques: Review of a Decade

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