scholarly journals Forecasting exchange rates using feedforward and recurrent neural networks

1995 ◽  
Vol 10 (4) ◽  
pp. 347-364 ◽  
Author(s):  
Chung-Ming Kuan ◽  
Tung Liu
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Recurrent Neural Networks

Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates

1996 ◽  
Vol 3 (1) ◽  
pp. 59-75 ◽  
Author(s):  
Giovani Dematos ◽  
Milton S. Boyd ◽  
Bahman Kermanshahi ◽  
Nowrouz Kohzadi ◽  
Iebeling Kaastra
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Recurrent Neural Networks ◽  
Japanese Yen

Forecasting Short-Term Exchange Rates

2011 ◽  
pp. 195-212
Author(s):  
Leong-Kwan Li ◽  
Wan-Kai Pang ◽  
Wing-Tong Yu ◽  
Marvin D. Troutt
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Recurrent Neural Networks ◽  
Foreign Exchange Rates ◽  
Short Term ◽  
Financial Strategy ◽  
Term Trend ◽  
Exchange Markets ◽  
Short Term Trend

Movements in foreign exchange rates are the results of collective human decisions, which are the results of the dynamics of their neurons. In this chapter, we demonstrate how to model these types of market behaviors by recurrent neural networks (RNN). The RNN approach can help us to forecast the short-term trend of foreign exchange rates. The application of forecasting techniques in the foreign exchange markets has become an important task in financial strategy. Our empirical results show that a discrete-time RNN performs better than the traditional methods in forecasting short-term foreign exchange rates.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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