Stock Price Prediction based on CNN-LSTM Model in the PyTorch Environment

DOI

10.2991/978-94-6463-036-7_188How to use a DOI?

Keywords

Stock price prediction; PyTorch; CNN; LSTM

Abstract

The stock market, as the main financing channel for listed companies and the most accessible wealth creation opportunity for investors, has always attracted attention from all walks of life. With the evolution of the technology, deep learning has started to play a very important role in forecasting stock price. Based on in-depth research on CNN and LSTM, this paper builds a CNN-LSTM stock price prediction model in PyTorch environment, and takes the data from the A-share market, choosing Shanghai Composite Index for a total of ten years from January 2012 to December 2021 as the experimental object, then verifying the feasibility of this joint model in the field of stock price forecasting, while comparing with the predicted values obtained using CNN and LSTM alone. The result confirms that the CNN-LSTM joint model performs well.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Weidong Xu
PY  - 2022
DA  - 2022/12/31
TI  - Stock Price Prediction based on CNN-LSTM Model in the PyTorch Environment
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 1272
EP  - 1276
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_188
DO  - 10.2991/978-94-6463-036-7_188
ID  - Xu2022
ER  -