BiCuDNNLSTM-1dCNN — A hybrid deep learning-based predictive model for stock price prediction

Kanwal, Anika; Lau, Man Fai; Ng, Sebastian; Sim, Kwan Yong; Chandrasekaran, Siva

doi:10.1016/j.eswa.2022.117123

Cited by 68 publications

(26 citation statements)

References 37 publications

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“…However, integrating multiple models can lead to increased complexity, making them computationally expensive and more challenging to manage. 69,79,88 These models often necessitate tuning and validation, which can be resource intensive. DL hybrids might require substantial amounts of data for effective training, which might pose a limitation in certain scenarios.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Hybrid DL approaches frequently combine DL techniques with traditional methods [71][72][73][74][75] or DL architectures with each other, such as CNN-LSTM, LSTM or BiLSTM with attention mechanisms (AMs), transformer models, and graph convolutional neural network (GraphCNN). [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] These hybrid DL models prove to be efficient in identifying complex patterns and relationships in data due to the high capacity and adaptability of DL architectures, especially in applications like SPF. Chandar 71 proposed a new method for stock trading by combining technical indicators and CNNs, termed TI-CNN.…”

Section: Hybrid Approachesmentioning

confidence: 99%

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A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Hybrid Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

View full text Add to dashboard Cite

show abstract

“…The output of the 1D CNN, which is a sequence of feature vectors, is then fed into a recurrent layer that captures temporal dependencies and long‐term patterns in the feature map. It is worth noting that the recurrent layers can be placed before the convolutional layers when temporal dependencies within the input sequence are considered crucial and must be modeled before extracting spatial features (Kanwal et al, 2022; Zhang et al, 2021).…”

Section: Deep Learning Models For Price Forecastingmentioning

confidence: 99%

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Zhang,

Sjarif,

Ibrahim

2023

WIREs Data Min & Knowl

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Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

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“…The methods of stock forecasting mainly focus on deep learning and fusion models. Deng, C. R., et al Developed a hybrid stock price index prediction modeling framework using long-term and short-term memory (LSTM) and multivariate empirical mode decomposition (MEMD), which can capture the intrinsic characteristics of the complex dynamics of the stock price index time series [13]; Gao, R. Z., et al Proposed a deep learning method combined with genetic algorithm to predict the target stock market index [14]; Gao, Z., et al Proposed a prediction algorithm integrating multiple support vector regression (SVR) models, and used reasonable weight to combine the prediction results of multiple models to improve the accuracy of the model [15]; Gupta, U., et al In order to overcome the problem of overfitting, a new data enhancement method was proposed in the StockNet model based on Gru [16]; He, Q. Q., et al proposed a new case-based deep transfer learning model with attention mechanism [17]; Kanwal, A., et al Proposed a prediction model based on hybrid deep learning (DL), which combines deep neural network, short-term memory and one-dimensional convolutional neural network (CNN) [18]; Kumar, R., et al Proposed a three-stage fusion model to process time series data and improve the accuracy of stock market prediction [19]; Li, R. R., et al Proposed a multi-scale modeling strategy based on machine learning methods and econometric models [20].…”

Section: Related Work 21 Research On Univariate Time Seriesmentioning

confidence: 99%

A financial time series data mining method with different time granularity based on trend Division

Yang

Gao

Liu

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Stock research is an important field of Finance and time series research. Stock data research is also a typical financial time series problem. In the research of financial time series, there are many methods, such as model building, data mining, heuristic algorithm, machine learning, deep learning, and so on. VAR, ARIMA and other methods are widely used in practice. ARIMA and its combination methods have good processing effect on small data sets, but there are over fitting problems, which are difficult to process large data sets and data with different time granularity. At present, this paper takes the decision table transformation method of financial time series data as the research object, and puts forward the trend division method of financial time series based on different time granularity through the trend division of financial time series. On this basis, it puts forward the trend extreme point extraction method, and constructs the stock time series decision table according to the extreme point information and combined with the stock technical indicators, The decision table is verified by support vector machine based on the decision table. The research shows that the trend division method under different time granularity can transform the extreme point information into a decision table, which will not produce over fitting problem in practical application. It is an effective time series processing method, and provides a new research method for the future time series research with different granularity.

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BiCuDNNLSTM-1dCNN — A hybrid deep learning-based predictive model for stock price prediction

Cited by 68 publications

References 37 publications

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

A financial time series data mining method with different time granularity based on trend Division

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