Stock Market Prediction Time Series Analysis Using Stacked LSTM Model

Volume 9, Issue 2, April 2024     |     PP. 47-59      |     PDF (1417 K)    |     Pub. Date: May 28, 2024
DOI: 10.54647/computer520418    31 Downloads     3147 Views  

Author(s)

Parnandi SrinuVasarao, Department of Computer Science and Engineering, Lincoln University College,Malaysia
Midhun Chakkaravarthy, Department of Computer Science and Engineering, Lincoln University College,Malaysia

Abstract
Stock Market prediction has long been a challenging task due to its complex and dynamic nature. Time series analysis using Long Short-Term Memory(LSTM) neural network has merged as a promising approach for predicting stock prices. This research aims to investigate the effectiveness of LSTM models in predicting stock market trends and to explore their potential for generating actionable in sights for traders and investors. This study utilizes historical stock price data to train and evaluate LSTM models for predicting future stock prices, utilizing various hyperparameters and model configurations for optimal performance. The study demonstrates the effectiveness of LSTM-based time series analysis in stock market prediction, indicating its practical application for traders and investors in volatile markets, but acknowledges uncertainty and needs further research.

Keywords
Predictive, Time series, market trend, sentimental analysis, model optimization, LSTM

Cite this paper
Parnandi SrinuVasarao, Midhun Chakkaravarthy, Stock Market Prediction Time Series Analysis Using Stacked LSTM Model , SCIREA Journal of Computer. Volume 9, Issue 2, April 2024 | PP. 47-59. 10.54647/computer520418

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