Stacked LSTM a Deep Learning model to predict Stock market

Jaswanth, Bandi; Kaushik, Jitendra

doi:10.1109/icac3n56670.2022.10074222

Cited by 1 publication

(1 citation statement)

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“…The literature suggests that stacked LSTM is a promising deep learning model for stock prediction. Jaswanth and Kaushik [19] and Zhang et al [20] delved into utilizing stacked LSTM in stock prediction and demonstrating enhanced predictive power and generalization capabilities. Uddin et al [21] proposed an integrated solution that combines an extended LSTM model with a multivariate feature correlation approach, showing promising potential in stock prediction.…”

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%