Forecasting Apple Stock Closed Prices by LR and LSTM with Discrete Wavelet Transformation

DOI

10.2991/978-94-6463-036-7_138How to use a DOI?

Keywords

LR; DWT; LSTM; Apple; Forecast

ABSTRACT

Stock prediction has long had a high profile among investors under the incentives of profit maximization. However, as a result of the instability and chaos of the financial stock market, predicting stock prices is challenging. To address this problem, the discrete wavelet transformation (DWT) is applied to denoise stock prices when data preprocessing. Long short-term memory (LSTM) and linear regression model (LR) are chosen to train the model. The performances of LR, LSTM, the combination of DWT and LR and the combination of DWT and LSTM are demonstrated and compared when predicting the Apple stock closed prices by using its rescaled closed price five days ago. The prediction results proved the effectiveness of DWT and illustrated LR still acts well although it is much simpler compared with LSTM in terms of RMSE, MAE, MAPE. These model- based analytic strategies and pre-programmed stock price prediction are likely to give precious guidance to investors in the pursuit of maximum benefits.

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.

Download article (PDF)

TY  - CONF
AU  - Yuxin Yang
PY  - 2022
DA  - 2022/12/31
TI  - Forecasting Apple Stock Closed Prices by LR and LSTM with Discrete Wavelet Transformation
BT  - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)
PB  - Atlantis Press
SP  - 935
EP  - 943
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-036-7_138
DO  - 10.2991/978-94-6463-036-7_138
ID  - Yang2022
ER  -