Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model

Ali, Muhammad; Khan, Dost Muhammad; Alshanbari, Huda M.; El-Bagoury, Abd Al-Aziz Hosni

doi:10.3390/app13031429

Cited by 43 publications

(20 citation statements)

References 53 publications

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“…MAE is a measure of how far the predicted value is from the actual value by calculating the absolute value of the difference between the two and then taking the average [29]. To calculate MAE, we can use (8) [30]. MAPE is a good measure to assess the performance of a regression model because it can show how much error the average prediction has compared to the actual value.…”

Section: Performance Evaluationmentioning

confidence: 99%

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Priyatno,

Ramadhan Sudirman,

Musridho

2024

IJECE

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

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Section: Performance Evaluationmentioning

confidence: 99%

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Priyatno,

Ramadhan Sudirman,

Musridho

2024

IJECE

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“…LSTM is found to be inefficient when working with smaller datasets, which is often considered a drawback by certain researchers, such as Sheth and Shah (2023). The LSTM model has been also hybridized in the prediction of the non-stationary and non-linear stock market (see Ali et al (2023)). Apart from LSTM, GRU has also been explored in stock prediction quite extensively (see Qi et al (2023)).…”

Section: Related Workmentioning

confidence: 99%

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Dip Das,

Thulasiram,

Henry

et al. 2024

JRFM

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This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

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“…The MFCC method is used to convert signals into spectral images. ResNet101 and VGG16 are two feature extractors with DCNN classifiers [28][29][30]. This approach incorporated CNN classification, respiratory sound, and pre-trained image recognition.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

Gulzar¹,

Li²,

Manzoor³

et al. 2023

IJBB

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With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. We used a technique called MelFrequency Cepstral Coefficients (MFCCs). Here, features are retrieved and categorized via VGG16 (transfer learning) and prediction is accomplished using 5-fold cross-validation. Employing various data splitting techniques, Respiratory Sound Database obtained cutting- edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%. The ICBHI dataset is used to train and test the model.

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Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model

Cited by 43 publications

References 53 publications

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Transfer Learning based Diagnosis and Analysis of Lung Sound Aberrations

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