An Approach to Optimization of Gated Recurrent Unit with Greedy Algorithm - Lazcano Muñoz, Patricio Ignacio (Journal of Computer and Forensic Sciences, 2024)
This study focuses on enhancing the performance of Stacked Gated Recurrent Unit (GRU) model in time series data processing, specifically in stock price prediction. The most significant innovation occurs in the integration of a Greedy Algorithm for optimizing hyperparameters such as look-back period, number of epochs, batch size, and units in each GRU layer. Historical stock data from Apple Inc. is utilized for the model's application, emphasizing the model's effectiveness in predicting stock prices. The study methodology involves a sequence of steps, such as data loading, preprocessing, dataset splitting, model construction and evaluation. The role of the Greedy Algorithm's focuses in iteratively adjusting hyperparameters to minimize the Root Mean Squared Error (RMSE) metric, resulting in refining the model's predictive accuracy. The outcomes reveal that the integrated Greedy Algorithm significantly enhances the model's accuracy in predicting stock prices, indicating its potential application in various scenarios requiring precise time series forecasting.
Keywords: Stacked Gated Recurrent Unit, Greedy Algorithm, Time Series Prediction, Hyperparameter Optimization, Stock Price Forecasting.