A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
- Title
- A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
- Creator
- Agarwal V.; Ravi Kumar P.; Shankar S.; Praveena S.; Dubey V.; Chauhan A.
- Description
- Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. 2023 IEEE.
- Source
- Proceedings of the 7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023, pp. 78-82.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolutional Neural Network (CNN); Extreme Learning Machine (ELM); Kernel Extreme Learning Machine (KELM)
- Coverage
- Agarwal V., ATLAS SkillTech University, ISME, Maharashtra, Mumbai, India; Ravi Kumar P., Kristu Jayanti College (Autonomous), K.Narayanapura, Department of Media Studies, Karnataka, Bangalore, India; Shankar S., Computational Intelligence Srmist Kattankulathur, Tamil Nadu, Chengalpattu, India; Praveena S., Mahatma Gandhi Institute of Technology (MGIT), Department of Electronics and Communication Engineering, Telangana, Hyderabad, India; Dubey V., Computer Science and Engineering, Bharat Institute of Engineering and Technology, Telangana, Hyderabad, India; Chauhan A., CHRIST (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039725-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Agarwal V.; Ravi Kumar P.; Shankar S.; Praveena S.; Dubey V.; Chauhan A., “A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19923.