A Study of LSTM in Stock Price Prediction

Bairagi, Ishan Adhikari; Daniel, Asha Elizabeth; Sharma, Anchal

doi:10.1109/icac3n56670.2022.10074373

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…The proposed method achieves impressive results, with Mean Absolute Percentage Error (MAPE) values of 1.279%, 1.564%, and 2.047% for BVSP, IBM, and AAPL stocks, respectively. Similarly, Bairagi et al [9] focused on utilizing LSTM neural networks to forecast the prices of selected stocks and develop a user-friendly stock analysis dashboard. Meanwhile, Wu et al [10] proposed an innovative framework that combines Convolutional Neural Network (CNN) and LSTM to enhance stock price prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory

Diqi,

Ordiyasa,

Hamzah

2024

ITJRD

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This research explores the Stacked Long Short-Term Memory (LSTM) model for stock price prediction using a dataset obtained from Yahoo Finance. The main objective is to assess the effectiveness of the model in capturing stock price patterns and making accurate predictions. The dataset consists of stock prices for the top 10 companies listed in the Indonesia Stock Exchange from July 6, 2015, to October 14, 2021. The model is trained and evaluated using metrics such as RMSE, MAE, MAPE, and R2. The average values of these metrics for the predictions indicate promising results, with an average RMSE of 0.00885, average MAE of 0.00800, average MAPE of 0.02496, and an average R2 of 0.9597. These findings suggest that the Stacked LSTM model can effectively capture stock price patterns and make accurate predictions. The research contributes to the field of stock price prediction and highlights the potential of deep learning techniques in financial forecasting.

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Section: Introductionmentioning

confidence: 99%