Stock Price Analysis and Prediction Using Seq2Seq LSTM-Reference-Cited by-同舟云学术

Stock Price Analysis and Prediction Using Seq2Seq LSTM

Published:2023 Issue: Volume: Page:655-666
ISSN:2367-3370
Container-title:Proceedings of International Conference on Data Analytics and Insights, ICDAI 2023
language:
Short-container-title:

Author:

Dash Aniket,Singh Aman,Jain Akshat,Shukla Anand,Mishra Himanshu,Vyas Piyush,Kaur Sandeep

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-3878-0_56

Reference14 articles.

1. Abrishami S, Turek M, Choudhury AR, Kumar P (2019) Enhancing profit by predicting stock prices using deep neural networks. In: 2019 IEEE 31st International conference on tools with artificial intelligence (ICTAI). IEEE, pp 1551-1556

2. Subashini A, Karthikeyan M (2018) Forecasting on stock market time series data using data mining techniques, pp 6–13. www.ijesi.org

3. Adebiyi AA, Adewumi AO, Ayo CK (2014) Comparison of ARIMA and artificial neural networks models for stock price prediction. J Appl Math

4. Karlsson I, Rebane J, Papapetrou P, Denic S (2018) Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: a comparative study. In: SIGKDD Fintec'18

5. Clerk Maxwell J (1892) A treatise on electricity and magnetism, 3rd ed, vol 2. Clarendon, Oxford, pp 68-73. https://www.mdpi.com/2076-3417/10/7/2402

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing portfolio management using artificial intelligence: literature review;Frontiers in Artificial Intelligence;2024-04-08

2. A Comparative Analysis of Time Series Forecasting Methods for Short-Term Electricity Demand Prediction;2023 14th International Conference on Information and Communication Technology Convergence (ICTC);2023-10-11