Volume - 6 | Issue - 4 | december 2024
Published
16 November, 2024
This research offers an in-depth examination of predicting the closing prices of the metal ores industry index on the Tehran Stock Exchange (TSE) using a Gated Recurrent Unit (GRU) model. The GRU, a type of recurrent neural network, shows great promise for tasks involving time series forecasting. The historical daily price data from October 2017 to October 2022, was used in the study after carefully preprocessing it for further analysis. The study begins with a univariate analysis to reveal distribution characteristics and the relationships between essential variables. A customized GRU model that is trained on 70% of the time series data, with its performance assessed through metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and the R-squared (R2) score is used for prediction. The results indicate that the GRU model provides accurate predictions for the metal ores industry index, outperforming traditional forecasting techniques. The model's recurrent nature enables it to capture both short-term and long-term temporal dependencies within the data. This research highlights the significant potential of GRU networks in the realm of financial forecasting. Future improvements will focus on hyperparameter optimization and further integrating additional input variables to enhance predictive accuracy.
KeywordsStock Market Prediction Gated Recurrent Unit Metal Ores Industry Tehran Stock Exchange Deep Learning Recurrent Neural Networks
