Forecasting exchange rate using deep belief networks and conjugate gradient method

By Shen Furao, Jing Chao, Jinxi Zhao. 2015

233 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research, Probabilistic & Bayesian Methods, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “Similarly, the authors in [171] implemented several different DBN networks to predict weekly GBP/USD, BRL/USD and INR/USD exchange rate returns.”
Forecasting Crude Oil Prices: a Deep Learning based Model (2017, background): “The dynamic complicated interactions among these factors result in the mysterious behavior of the crude oil price movement, whose characterization and prediction 1.”
Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, methodology): “Nonetheless, it is also used in mechanical engineering classi-ﬁ cation of defects in compressor valves [45] and in ﬁ nance forecasting of the exchange rate [46].”
Time series prediction of stock price using deep belief networks with intrinsic plasticity (2017, background): “problems, such as classification[12][13][14], voice activity detection[15][16][17], information retrieval[18], traffic flow prediction[19], and forecasting exchange rate[20].”
Research on exchange rate forecasting based on deep belief network (2017, unknown): “[8] expanded restricted Boltzmann machine (RBM) existing every layer of DBN to continuous restricted Boltzmann machines (CRBMs) [9].”
Convolutional neural networks applied to high-frequency market microstructure forecasting (2017, background): “Shen, Chao and Zhao [8] used Deep Belief Networks for forecasting foreign exchanges rates and were able to outperform a classical feedforward network.”
A Time Series Forecasting Model Based on Deep Learning Integrated Algorithm with Stacked Autoencoders and SVR for FX Prediction (2016, background): “While until now there are few people make empirical study of time series modeling with the typical deep neural network naming Stacked Autoencoder(SAE)[7-8], which consists of multiple layers of sparse autoencoders and the outputs of each layer is wired to the inputs of the successive layer[9-10].”
Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability (2017, methodology): “Recently, artiﬁcial neural network-based prediction models have again been attracting attention owing to the development of parallel processing technology, as well as algorithms that overcome the limitations of deep neural networks [11–13].”
An innovative neural network approach for stock market prediction (2018, background): “It also indicated that the predictive effect of DBN could reach 63% better than 41% of FFNN [23].”