An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis

Swathi, T.; Kasiviswanath, N.; Rao, A. Ananda

doi:10.1007/s10489-022-03175-2

Cited by 79 publications

(32 citation statements)

References 32 publications

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“…Therefore, LSTM partly overcomes the problems of vanishing and exploding gradients of the traditional recurrent neural network. LSTM selects the information to be remembered and the information to be forgotten through memorizing (input gate, forgetting gate, and output gate) and two states (cellular state and hidden state), so as to make long-term dependence of network learning . Due to the memory function of LSTM, LSTM is gradually applied to industrial time series analysis and prediction.…”

Section: Proposed Methodologymentioning

confidence: 99%

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

et al. 2023

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Solvent deasphalting (SDA) is a complex multiscale continuous process. The operation mode of the SDA process is not considered in the related data-driven model. Therefore, this paper proposes a time lag process prediction model with multiple operation modes to solve the above problem. First, based on random forests, the relative importance of initial input variables in the SDA process on DAO yield and Conradson carbon residual are studied and features are selected according to the results. Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction. SDAE can improve clustering accuracy of fuzzy c-means, and the operation mode of SDA process is accurately divided. Long short-term memory (LSTM) is used to establish a multicondition LSTM model. Compared with the traditional LSTM model, the multicondition LSTM model has a higher prediction accuracy with R 2 > 0.95. The sensitivity analyses of the properties of feed and operating conditions on DAO yield are consistent with the principle of two-phase countercurrent extraction in the SDA process. In addition, the benchmark test of the Tennessee Eastman process shows that the proposed method is also effective in the fault detection of other processes. Because the multicondition LSTM can predict the future process measurement data according to operating mode, it can better avoid the false alarm problem and predict the fault earlier.

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Section: Proposed Methodologymentioning

confidence: 99%

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

et al. 2023

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“…used a generative adversarial network for stock market price movement prediction. Another set of efforts used sentiment analysis of Twitter data for stock price prediction (Darapaneni et al, 2022;Jing et al, 2021;Mohan et al, 2019;Rao & Srivastava, 2012;Shivaprasad and Shetty, 2017;Swathi et al, 2022;Yusof et al, 2018). Sirignano and Cont (2019) further claim that the "general" model can also be used for "transfer" learning purposes.…”

Section: Deep Learning Applications In Operation Researchmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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“…LSTM has been used in various applications, even in the teaching sector. As an example, the study in [24] presented a novel teaching and learning optimization model that implemented LSTM bases sentimental analysis for stock price prediction using twitter data. The LSTM network helped to classify tweets considering positive and negative sentiments relevant to stock prices.…”

Section: Modelmentioning

confidence: 99%

Design of English Teaching Sharing System Combining Internet of Things and Memory Mechanism

Guo

2022

Wireless Communications and Mobile Computing

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In the era of Internet of Everything, English teaching has evolved continuously undergoing radical changes. Informatization and modern technologies are continuously promoting the reform of education, and the application of these technological tools makes information sharing a reality. Timely information sharing contributes to the development of English teaching as a part of curriculum requirements. As a consequence of this rapid progress, the theoretical and practical research on IoT-based English teaching sharing systems is also increasing. In this paper, we initiate an exhaustive research of the popular methodologies and then adopt LSTM network management platform data to address the shortcomings of the current teaching system has memory mechanism issues. At the outset, the standard English curriculum is discussed to establish the background, and then, data fusion among various types of information is considered. This is done by completely utilizing independent and scattered heterogeneous data in the information platform without changing or minimizing the information available in the shared system. Then, the information fusion is thoroughly analyzed resulting in the proposal of a multisource and multigranularity information fusion method based on a neural network. The method implements a study considering multisource heterogeneous data, which is followed by carrying out of functional design and process design. The paper also conducts a study on CNN-based multisource information association and designs information association algorithms to customize the dataset. Finally, the study of multigranularity feature fusion based on CNN and LSTM is implemented. The design relevant to granularity calculation algorithm helps in the customization of the dataset. The multigranularity feature fusion is conducted integrating CNN and LSTM. The experimental results show that the proposed method effectively integrates the scattered teaching information in the system, which is conducive to improving the utilization rate of teaching resources and simultaneously promoting the development of teaching information.

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An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis

Cited by 79 publications

References 32 publications

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

Yield and Properties Prediction Based on the Multicondition LSTM Model for the Solvent Deasphalting Process

Incorporating causality in energy consumption forecasting using deep neural networks

Design of English Teaching Sharing System Combining Internet of Things and Memory Mechanism

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