Sukestiyarno, Y.L.; Wiyanti, D.T.; Azizah, L.; Widada, W. Algorithm Optimizer in GA-LSTM for Stock Price Forecasting. Contemporary Mathematics 2024, doi:10.37256/cm.5120243367.
Sukestiyarno, Y.L.; Wiyanti, D.T.; Azizah, L.; Widada, W. Algorithm Optimizer in GA-LSTM for Stock Price Forecasting. Contemporary Mathematics 2024, doi:10.37256/cm.5120243367.
Sukestiyarno, Y.L.; Wiyanti, D.T.; Azizah, L.; Widada, W. Algorithm Optimizer in GA-LSTM for Stock Price Forecasting. Contemporary Mathematics 2024, doi:10.37256/cm.5120243367.
Sukestiyarno, Y.L.; Wiyanti, D.T.; Azizah, L.; Widada, W. Algorithm Optimizer in GA-LSTM for Stock Price Forecasting. Contemporary Mathematics 2024, doi:10.37256/cm.5120243367.
Abstract
Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is 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 valuesbelow 10% in every optimizerused. 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.
Keywords
Time Series; Forecasting, Deep Learning; Genetic Algorithm; Long Short-Term Memory
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter:
The commenter has declared there is no conflict of interests.