Comprehensive comparison of LSTM variations for the prediction of chaotic time series


2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021, Kocaeli, Turkey, 25 - 27 August 2021 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/inista52262.2021.9548647
  • City: Kocaeli
  • Country: Turkey
  • Keywords: Chaotic time series, Deep neural networks, Long short term memory (LSTM) variations, Lorenz system, Rössler system
  • Yıldız Technical University Affiliated: Yes


© 2021 IEEE.Chaos theory is a field of study used to model the irregular fluctuations encountered in all areas of our daily life. Time series obtained from chaotic systems such as currencies, stock markets, and weather conditions are called chaotic time series. Making predictions from these time series has recently attracted the attention of researchers as it will eliminate the need for complex mathematical models. Recently, deep neural networks are widely used in the prediction of chaotic time series as well as in solving many problems in the literature. In this study, time series obtained from three different chaotic systems were collected in order to predict chaotic time series, and four different LSTM variations were used to predict these data. As a result of the experimental studies, it has been observed that Stacked LSTM predicts more successfully than other LSTM variations. The obtained 3.7397x10-5 validation RMSE value and 0.1558 test RMSE value show that Stacked LSTM gives more accurate results than other methods used in the prediction of chaotic time series in the literature.