Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Authors

  • YL Sukestiyarno Universitas Negeri Semarang, Kelud Utara III Petompon Gajahmungkur Semarang 50229, Indonesia
  • Dian Tri Wiyanti Universitas Negeri Semarang, Kelud Utara III Petompon Gajahmungkur Semarang 50229, Indonesia
  • Lathifatul Azizah Universitas Negeri Semarang, Kelud Utara III Petompon Gajahmungkur Semarang 50229, Indonesia
  • Wahyu Widada Bengkulu University, Jl. Kd. Limun, Bengkulu 38371, Indonesia
  • Khathibul Umam Zaid Nugroho Universitas PGRI Silampari, Jl. M. Toha, Lubuk Linggau 31625, Indonesia

Keywords:

Time Series, Forecasting, Deep Learning, Genetic Algorithm, Long Short-Term Memory

Abstract

The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) to find a suitable model for predicting stock prices. GAis used to optimize the architecture, such as the number of epochs, window size, and LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. The method that has been applied shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. A fairly stable and small value is generated by setting it using the Adam Optimizer.

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Published

2024-01-05

How to Cite

1.
Sukestiyarno Y, Wiyanti DT, Azizah L, Widada W, Nugroho KUZ. Algorithm Optimizer in GA-LSTM for Stock Price Forecasting. Contemp. Math. [Internet]. 2024 Jan. 5 [cited 2024 Jun. 17];5(2). Available from: https://ojs.wiserpub.com/index.php/CM/article/view/3367