An intelligent forex monitoring system
A. Abraham,Morshed U. Chowdhury +1 more
- Vol. 3, pp 523-528
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TLDR
This work attempts to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates.Abstract:
The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network.read more
Citations
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Journal ArticleDOI
Soft computing hybrids for FOREX rate prediction: A comprehensive review
TL;DR: It is conspicuous from the review that artificial neural network based hybrids turned out to be more prevalent, more pervasive and more powerful than other soft computing hybrids found in the literature.
Proceedings Article
Analysis of hybrid soft and hard computing techniques for forex monitoring systems
TL;DR: This work attempts to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead and it is observed that the proposed hybrid models could predict the Forex rates more accurately than all the techniques when applied individually.
Proceedings ArticleDOI
Analysis of hybrid soft and hard computing techniques for forex monitoring systems
TL;DR: In this paper, the authors compared the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead, and observed that the proposed hybrid models could predict the forex rate more accurately than all the techniques when applied individually.
Journal ArticleDOI
Multi-scale Foreign Exchange Rates Ensemble for Classification of Trends in Forex Market
TL;DR: A new classification method for identifying up, down, and sideways trends in Forex market foreign exchange rates is proposed and results show superiority of ensemble classifier over individual ones.
Journal ArticleDOI
A Review on Recent Advancements in FOREX Currency Prediction
TL;DR: This research presents a comprehensive review of the recent advancements of FOREX currency prediction approaches and shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM) have been fully explored and show huge potential in time series prediction.
References
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Book
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
TL;DR: This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.
Journal ArticleDOI
Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]
TL;DR: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming.
Journal ArticleDOI
Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning
TL;DR: Experiments show that SCG is considerably faster than BP, CGL, and BFGS, and avoids a time consuming line search.
Posted Content
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
TL;DR: This paper broadly classify the integration of ANN and FIS into three categories namely concurrent model, cooperative model and fully fused model, and focuses on the different types of fused neuro-fuzzy systems.
Book ChapterDOI
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
TL;DR: Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems as discussed by the authors.