A Comparative Study of Various LSTM Models for Stock Market Time Series Classification

It is difficult to make precise forecasts about changes in stock prices since the stock market is a dynamic and intricate system that is impacted by many different elements. This document emphasizes using Long Short-Term Memory (LSTM) neural networks, a subset of Recurrent Neural Networks (RNNs), to analyze historical time series data to compare changes in stock prices. In particular we examine and compare the functionality of three LSTM architectures: stacked LSTM, bidirectional LSTM, and vanilla LSTM. Key performance indicators like accuracy, precision, recall, and F1-score are used to measure each model’s performance once it has been trained on stock market data. The findings show that the Stacked LSTM model outperforms the Vanilla and Bidirectional LSTM models in terms of classification performance and accuracy. However, the increased computational complexity of the Stacked LSTM comes with a trade-off in terms of training time and resource usage. Additionally, this study demonstrates how well LSTM-based models can forecast stock price patterns and makes recommendations for future paths, such as including sentiment analysis, To improve prediction accuracy even more, hybrid architectures and Transformer models are being investigated. Overall, this work demonstrates the efficacy of LSTM models for stock market time series classification and provides a foundation for future improvements in the field.