Deep Learning-based Gold Price Prediction: A Novel Approach using Time Series Analysis

This paper presents a deep learning-based system for predicting gold prices using historical data. The system leverages Long Short-Term Memory (LSTM), a specialized recurrent neural network architecture, to capture temporal dependencies and patterns in the time series data of gold prices. A comprehensive dataset of historical gold prices is used, and the model is trained on a sequence of past data points to predict future prices. The data is preprocessed using normalization techniques to improve the performance of the model. Experimental results demonstrate the effectiveness of the proposed model in providing accurate price predictions, offering potential utility in financial forecasting and decision-making processes. The system's performance is evaluated through visualization and statistical metrics, illustrating its capacity to track gold price trends and predict future market movements. This work contributes to the growing field of time series forecasting by applying deep learning techniques to financial markets.