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


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



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


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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How to Cite

Al Haromainy MM, Prasetya DA, Sari AP. Improving Performance of RNN-Based Models With Genetic Algorithm Optimization For Time Series Data. TIERS [Internet]. 2023Jun.25 [cited 2024May18];4(1):16-24. Available from:




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