Predicting Stock Prices using LSTM Neural Networks

The development of deep learning LSTM, BiLSTM, and NeuralProphet models for multi-step stock price forecasting.

Predicting stock prices is a challenging task due to the complex and volatile nature of financial markets. However, with the advancements in deep learning techniques, LSTM (Long Short-Term Memory) neural networks have shown promise in capturing patterns and making accurate predictions. In this project, we explore the use of LSTM neural networks to predict stock prices and evaluate their performance.

We begin by preprocessing the stock price data and splitting it into training and test sets. The LSTM model is then constructed, utilizing bidirectional LSTM layers to capture both past and future information. The model is trained on the training data, and the loss is optimized using the Adam optimizer. We employ early stopping to prevent overfitting and achieve the best generalization performance.

To assess the performance of the model, we compute various evaluation metrics such as RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error) on both the training and test sets. Additionally, we analyze the differences between the predicted and actual values to identify outliers or anomalies in the predictions.

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