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

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

Neural Networks for Technical Forecasting of Foreign Exchange Rates

2002 ◽  
pp. 189-204
Author(s):  
Jing Tao Yao ◽  
Chew Lim Tan
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Series Data ◽  
Technical Indicators ◽  
Currency Exchange ◽  
The Neural Network ◽  
Out Of Sample ◽  
Japanese Yen

This chapter describes the application of neural networks in foreign exchange rate forecasting between American dollar and five other major currencies: Japanese yen, Deutsch mark, British pound, Swiss franc and Australian dollar. Technical indicators and time series data are fed to neural networks to mine, or discover, the underlying “rules” of the movement in currency exchange rates. The results presented in this chapter show that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profit can be achieved for out-of-sample data with simple technical indicators. The neural-network-based forecasting is also shown to compare favorably with the traditional statistical approach.

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

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.

Spike timing-dependent plasticity in sparse recurrent neural networks

2014 ◽  
Vol 1 ◽  
pp. 485-488
Author(s):  
Hideyuki Kato ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

Direct Adaptive Control of Process Systems Using Recurrent Neural Networks

1992 ◽  
Author(s):  
Sanjay Parthasarathy ◽  
Alexander G. Parlos ◽  
Amir F. Atiya
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Recurrent Neural Networks ◽  
Process Systems ◽  
Direct Adaptive Control

L2 approximation properties of recurrent neural networks

1997 ◽  
Author(s):  
A. Ruiz ◽  
D.H. Owens ◽  
S. Townley
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Approximation Properties

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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