Intelligent Decision Technologies 2016

Czarnowski, Ireneusz; Mateos, Alfonso; Howlett, Robert J.; Jain, Lakhmi C.

doi:10.1007/978-3-319-39627-9

Cited by 9 publications

(2 citation statements)

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“…Eye-tracking studies of the processing of Russian texts are not numerous. They are mainly focused on the recognition of a regular verbal text (Bezrukikh and Ivanov, 2013, 2014Kornev et al, 2014;Petrova, 2016;Korneev et al, 2017a,b). There is only one eye-tracking research on Russian (Petrova and Riekhakaynen, 2019) in which the processing of a polycode text, namely infographics, has been studied.…”

Section: Resultsmentioning

confidence: 99%

An Eye-Tracking Study of Sketch Processing: Evidence From Russian

2020

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Section: Resultsmentioning

confidence: 99%

An Eye-Tracking Study of Sketch Processing: Evidence From Russian

2020

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“…The advantages of trading in the forex market include no commissions, no middlemen, no minimum lot size, low transaction costs, abundant liquidity, nearly instantaneous transactions, low margins or high leverage, and activities that are available 24 h a day. The entire market is divided into four overlapping sessions that take place in separate time zones, with the following hours stated in GMT + 1 time: London (8 am-4 pm); New York (1 pm-9 pm); Sydney (10 pm-6 am); Tokyo (midnight-8 am) [9]. Online trading is available, and insider trading is prohibited with few regulations [63].…”

Section: Introductionmentioning

confidence: 99%

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Ayitey

Appiahene

Appiah

2022

Journal of Electrical Systems and Inf Technol

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Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

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Self-learning Procedures for a Kernel Fuzzy Clustering System

Bodyanskiy

Tyshchenko

2018

Advances in Intelligent Systems and Computing

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Intelligent Decision Technologies 2016

Cited by 9 publications

References 0 publications

An Eye-Tracking Study of Sketch Processing: Evidence From Russian

An Eye-Tracking Study of Sketch Processing: Evidence From Russian

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Self-learning Procedures for a Kernel Fuzzy Clustering System

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