Autoencoder-Lstm Framework for Accurate Foreign Exchange Volatility Prediction

Accurately predicting foreign exchange volatility is crucial for financial institutions, traders, and policymakers. This makes it an extremely complex and dynamic market, hence failing traditional methods of prediction for capturing intricate patterns present in time-series data. Advanced machine learning models, have shown great potential in addressing this challenge. We propose an Autoencoder-Lstm model that leverages the strengths of both autoencoder for feature extraction and LSTM networks for time-series forecasting. These networks have gated mechanisms controlling the flow of information, particularly suitable for financial forecasting in their ability to extract long-term dependencies and trends which improve the forecast's accuracy as far as selective memory in prediction of volatility goes. The autoencoder simplifies input data by extracting important features and dropping out unnecessary noise. After extracting features from the dataset the network processes them efficiently while recognizing past time series connections. The research is implemented using Python software, for model development. The framework is evaluated using real-world foreign exchange data, incorporating both raw price features and derived features such as returns and volatility measures. The model is trained and tested on a dataset of daily forex rates, achieving an impressive prediction accuracy of 99.2%. Performance metrics, including RMSE (0.012), MAE (0.008), and MSE (0.00014), underscore the model's ability to minimize prediction errors, significantly outperforming traditional models. The results demonstrate the Autoencoder-Lstm’s effectiveness in predicting exchange rate volatility, making it a valuable tool for financial forecasting. This model's high accuracy and robustness further establish its potential for broader applications in other time-series prediction tasks.