Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach
- Title
- Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach
- Creator
- Basha M.S.A.; Senthil Kumar J.P.; Sucharitha M.M.; Ayesha S.; Laskhmi M.B.
- Description
- Experts in the finance system have long found it difficult to estimate stock values. Despite the Efficient - market hypothesis Principle claim that it is difficult to anticipate share prices with any degree of precision, research has demonstrated that share price movements could be anticipated with the proper levels of precision provided the correct parameters are chosen and the proper predictive models are created. individuals who are adaptable. The share market is unpredictable in essence, making its forecasting a difficult undertaking. Stock prices are affected by more than economic reasons. In this project, Arima, LSTM and Prophet models are used to predict the future way of behaving share price, the datasets has been obtained from NSE, share price prediction algorithms have been created and tested. According to the empirical findings, the LSTM model would be used to anticipate share prices rather well over a substantial amount of time with exactness. 2022 IEEE.
- Source
- 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- ARIMA; FB Prophet; LSTM; Share Price Prediction; Time series analysis
- Coverage
- Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to University), Bengaluru, India; Senthil Kumar J.P., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to University), Bengaluru, India; Sucharitha M.M., Christ (Deemed to University), Department of Professional Studies, Bengaluru, India; Ayesha S., Christ (Deemed to University), Department of Professional Studies, Bengaluru, India; Laskhmi M.B., Christ (Deemed to University), Department of Professional Studies, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166545342-4
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Basha M.S.A.; Senthil Kumar J.P.; Sucharitha M.M.; Ayesha S.; Laskhmi M.B., “Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/20111.