Forecasting the Metal Ores Industry Index on the Tehran Stock Exchange: A Gated Recurrent Unit (GRU) Approach DOI

Reza Javadpour Moghadam

Journal of Artificial Intelligence and Capsule Networks, Journal Year: 2024, Volume and Issue: 6(4), P. 436 - 451

Published: Nov. 16, 2024

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

Language: Английский

An optimized intrusion detection system for resource-constrained IoMT environments: enhancing security through efficient feature selection and classification DOI
Arash Salehpour, Mohammad Ali Balafar, Alireza Souri

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(6)

Published: April 27, 2025

Language: Английский

Citations

0
Forecasting the Metal Ores Industry Index on the Tehran Stock Exchange: A Gated Recurrent Unit (GRU) Approach DOI

Reza Javadpour Moghadam

Journal of Artificial Intelligence and Capsule Networks, Journal Year: 2024, Volume and Issue: 6(4), P. 436 - 451

Published: Nov. 16, 2024

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

Language: Английский

Citations

0