Chaotic Time Series Forecasting by using Echo State Network and Autoregressive Model
DOI:
https://doi.org/10.62933/faqd2g68Abstract
Chaotic time series forecasting such as maximum wind speed rates is of great importance in the fields of meteorology and renewable energy to reduce and control the harmful negative effects. The problem of wind speed is that it is affected by several interrelated factors such as temperature and atmospheric pressure, which are characterized by non-linearity through the influence of time series on differences that may be a cause of the emergence of uncertainty problems, which makes it difficult to model using traditional univariate time series methods. Echo State Network (ESN) is a neural network specialized in time series forecasting after addressing the problem of synchronization with the time variable as a recurrent network to address time-dependent effects and accurate prediction of time series in addition to its ability to model nonlinearly. This study presents the use of the Autoregressive (AR) model and then its hybridization with the deep echo state network, which is called the AR-ESN hybrid method by using the optimal structure of the AR model to determine the optimal inputs to the ESN network as the main contributions to solving the prediction problems for real data forecasts. The use of ESN as a proposed forecasting method is to improve the forecasting efficiency to reduce the risks associated with extreme weather fluctuations compared with traditional forecasting results. The results indicate that the ESN model based on AR model can contribute to increasing the forecasting accuracy of maximum wind speed compared with traditional models by using mean absolute percentage error (MAPE) as one of the criteria the forecasting accuracy.
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Copyright (c) 2025 Shahla Tahseen Hasan, Osamah Basheer Shukur (Author)
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