A New Stock Price Forecasting Method Using Active Deep Learning Approach

Alkhatib, Khalid; Khazaleh, Huthaifa; Alkhazaleh, Hamzah Ali; Alsoud, Anas Ratib; Abualigah, Laith

doi:10.3390/joitmc8020096

Cited by 37 publications

(12 citation statements)

References 76 publications

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“…In addition, we recommend that may researchers investigate the connection between the use of social media and the performance of the stock market. The economic climate that is created by the news media or the direct observation of the stock market sentiment can be used to make decisions re- In order to prove the effectiveness of CNN-LSTM, we compared this proposed deep learning model's result with the result of [51], who also used the companies Tesla and Apple. The results of the CNN-LSTM model proposed in this study compared to the models used in [51] are shown in Table 6.…”

Section: Discussionmentioning

confidence: 99%

“…The economic climate that is created by the news media or the direct observation of the stock market sentiment can be used to make decisions re- In order to prove the effectiveness of CNN-LSTM, we compared this proposed deep learning model's result with the result of [51], who also used the companies Tesla and Apple. The results of the CNN-LSTM model proposed in this study compared to the models used in [51] are shown in Table 6. The propped CNN-LSTM model is superior to the other study's models according to the MSE metric.…”

Section: Discussionmentioning

confidence: 99%

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Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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“…For example, in natural language processing (NLP), LSTM networks are used for text generation [ 55 ], translation [ 56 ], etc. In time series data, such as stock prices and weather prediction, LSTMs can be used for analysis and forecasting [ 57 , 58 ]. Similarly, LSTMs are highly effective for the detection of anomalies in time series data [ 59 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Sattar,

Mumtaz,

Qadir

et al. 2024

Sensors

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ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

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“…Remarkably, several studies have demonstrated the high level of accuracy of the machine learning methods [67][68][69][70], and they used these models for improving many problems. On the other hand, From the literature, most of the studies have investigated the relationship between oil prices and macroeconomic factors, but such studies seldom focus to validate the agreement on how much these macroeconomic factors influence oil prices [71].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

Fathalla¹,

Alameer²,

Abbas³

et al. 2023

Computer Systems Science and Engineering

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The oil industries are an important part of a country's economy. The crude oil's price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil's price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To examine the superiority of the proposed model, this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries (OPEC) oil price forecasting. Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods. More precisely, the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA (1,1,1), ARIMA (0,1,1), autoregressive moving average (ARMA) (0,1), vector autoregression (VAR), random walk (RW), support vector machine (SVM), and random forests (RF) models by 99.94%, 99.62%, 99.87%, 99.65%, 7.55%, 98.38%, and 99.35%, respectively, according to mean square error metric.

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A New Stock Price Forecasting Method Using Active Deep Learning Approach

Cited by 37 publications

References 76 publications

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

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