چکیده مقاله
Forecasting the stock market is a difficult undertaking because of the intricate and ever changing nature of financial markets Long Short Term Memory LSTM neural networks have displayed potential in grasping the time related relationships in financial data for predicting prices However, the effectiveness of LSTM models greatly depends on choosing the right hyperparameters This paper introduces an innovative method for fine tuning LSTM neural network hyperparameters by utilizing Taguchi Method to improve the accuracy of stock market predictions and additionally reduce the computational time required to obtain the best combination of hyperparameters In this research, we establish a Taguchi method driven optimization structure to automatically find the best combination of hyperparameters for LSTM models Evaluations are performed using open access datasets for stock markets with open prices
کلیدواژهها
نویسندگان
Hadi Hassanzadeh
dept of Industrial Engineering Ferdowsi University of Mashhad Mashhad, Iran
Alireza Hamedghafari
dept of Industrial Engineering Ferdowsi University of Mashhad Mashhad, Iran
Alireza Shadman
Associate Professor, dept of Industrial Engineering Ferdowsi University of Mashhad Mashhad, Iran
شیوه ارجاع
Hassanzadeh, Hadi and Hamedghafari, Alireza and Shadman, Alireza,1403,Tuning LSTM Neural Network Hyperparameters with Taguchi Method for Stock Market Prediction,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
مجموعه مقالات دهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها28 شهریور 1403 · مشهد