A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting
By Ren Zhang, Shen Furao, Jinxi Zhao. 2014
42 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research, Generative Models, Time-Series & Forecasting
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In Artificial Intelligence
In Electrical and Electronic Engineering
In Management Science and Operations Research
In Generative Models
In Time-Series & Forecasting
Citations in the corpus
- Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In [168], Fuzzy information granulation integrated with CDBN was applied for predicting EUR/USD and GBU/USD exchange rates.”
- Research on exchange rate forecasting based on deep belief network (2017, unknown): “For nations, forecasting the trend of exchange rate provides flexibility for developing appropriate exchange rate policy to avoid or mitigate the negative impact of exchange rate fluctuations on the domestic economy [1, 2].”
- 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].”