Foreign Exchange Prediction using CEEMDAN and Improved FA-LSTM

Ulina, Mustika; Purba, Ronsen; Halim, Arwin

doi:10.1109/icic50835.2020.9288615

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…To overcome these drawbacks, Torres et al proposed a more sophisticated algorithm: the DEMA. By incorporating a noise-assisted mechanism and an adaptive noise standard deviation, the DEMA algorithm improves the resilience and precision of EMD when dealing with non-stationary signals [22,23]. Its use has been widely recognized in the fields of signal processing and vibration analysis, demonstrating enhanced performance over the conventional EMD in certain cases.…”

Section: Dema Algorithmmentioning

confidence: 99%

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing

Zhang,

Wang,

Lyu

et al. 2024

Sensors

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Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach–Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

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Section: Dema Algorithmmentioning

confidence: 99%

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing

Zhang,

Wang,

Lyu

et al. 2024

Sensors

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“…Pada penelitian [8] membandingkan model yang LSTM dan CEEMDAN-LSTM. Pada mata uang AUD/USD dengan menggunakan hidden layer 32 dan batch size 50 dengan nilai dropout 0.04.…”

Section: Pendahuluanunclassified

Prediksi Harga Forex Menggunakan Algoritma Long Short-Term Memory

Pahlevi

2023

JNNLK

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Trillions of dollars per day of foreign currency trading activity occur in the forex market, which has very volatile movements in foreign currency trading. Trade based on bid and ask prices. The market determines foreign exchange rates based on supply and demand rules. Currency trading in pairs such as EUR/USD is a comparison of the value of the Euro against the Dollar as a basis for research, rising and falling currency prices in forex move fluctuatingly, so a market participant must be able to decide on buying and selling positions. Because wrong decisions can lead to losses. One of the ways to reduce risk in making decisions in buying and selling in forex can be using forecasting. This study uses the LSTM method in predicting forex prices which will be tested on several scales of dataset distribution. The smallest error results using a total dataset of 2631 with a dataset division of 70:15:15, which is divided into 70% data for training, 15% data for validation and 15% data for testing produces an RMSE value of 0.038, MAPE 2.5%. In measuring how well the regression model used with Rsquare on the data distribution is 70:15:15 and the total dataset used is 4979 to get the best results, namely 97%.

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“…According to Ulina et al [58], LSTM cells typically include four layers, three of which are "gates" that allow information to pass through optionally. The three gates that are frequently employed are the forget, input, and output gates.…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

“…The first section of the table compares our chosen dataset AUD/USD from April 1, 2013 to December 30, 2020 to the baseline MLP and LSTM models; the bolded results clearly illustrate that the proposed model beats the baseline models. From Table 1, the proposed model is compared with the work of Ulina et al [58], who proposed…”

Section: Two-layer Stacked Lstmmentioning

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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Foreign Exchange Prediction using CEEMDAN and Improved FA-LSTM

Cited by 8 publications

References 11 publications

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing

Prediksi Harga Forex Menggunakan Algoritma Long Short-Term Memory

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

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