Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data

Muhammad Muharrom Al Haromainy; Dwi Arman  Prasetya; Anggraini Puspita Sari

doi:10.38043/tiers.v4i1.4326

Authors

Muhammad Muharrom Al Haromainy UPN Veteran Jawa Timur
Dwi Arman Prasetya UPN Veteran Jawa Timur
Anggraini Puspita Sari UPN Veteran Jawa Timur

DOI:

https://doi.org/10.38043/tiers.v4i1.4326

Keywords:

Time Series Prediction, Recurrent Neural Networks (RNNs), Genetic Algorithms (GAs), Hyperparameter Optimization, Prediction Accuracy

Abstract

Stock price data or similar time series data can be used to carry out forecasting processes using past data. The method that can be used is like a neural network, one type of neural network that is used is the Recurrent Neural Network. When using the Recurrent Neural Network (RNN) method, we need to determine the appropriate parameters in order to get the best forecasting results. It takes experience or . In this study, this problem can be solved using optimization algorithms, such as Genetic Algorithms. With genetic algorithms, neural networks can be trained to get the best objective function. So that after implementing the RNN which was optimized using the Genetic Algorithm on stock time series data, when the trial was carried out without optimization the Genetic Algorithm got an RMSE value of 0.108, after being combined using the genetic algorithm it got an RMSE value of 0.106.

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