Algorithm Optimizer in GA-LSTM for Stock Price Forecasting

Authors

  • Yohanes Leonardus Sukestiyarno Universitas Negeri Semarang
  • Dian Tri Wiyanti Semarang State University, North Kelud III Petompon Gajahmungkur Semarang 50237, Indonesia
  • Lathifatul Azizah Semarang State University, North Kelud III Petompon Gajahmungkur Semarang 50237, Indonesia
  • Wahyu Widada

DOI:

https://doi.org/10.37256/cm.5120243367

Keywords:

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

Abstract

Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is represented by forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. 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 LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. 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 YL, Wiyanti DT, Azizah L, Widada W. Algorithm Optimizer in GA-LSTM for Stock Price Forecasting. Contemp. Math. [Internet]. 2024 Jan. 5 [cited 2024 Feb. 27];5(1). Available from: https://ojs.wiserpub.com/index.php/CM/article/view/3367

Issue

Section

Special Issue: ICCASA 2023