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Taylor-based optimized recursive extended exponential smoothed neural networks forecasting method

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Abstract

The development of a Time series Forecasting System is a major concern for Artificial Intelligence researchers. Commonly, existing systems only assess temporal features and analyze the behavior of the data over time, thus, resulting in uncertain forecasting accuracy. Although many forecasting systems were proposed in the literature; they have not yet answered the attending question. Hence, to overcome this problematic, we propose an innovative method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method, abbreviated as TOREESNN. Briefly explained, the proposed technique introduces three ideas to solve this issue: First, building an innovative framework for forecasting univariate time series based on Exponential Smoothed theory. Second, designing an Elman Classifier model for uncertainty prediction in order to correct the forecasted values. And finally hybrading the two recurrent systems in one framework to obtain the final results. Experimental results demonstrate that the proposed method has a high accuracy both in training and testing data in terms of Mean Squared Error (MSE) and outperforms the state-of-the-art Recurrent Neural Networks models on Mackey-Glass, Nonlinear Auto-Regressive Moving Average time series (NARMA), Lorenz, and Henon map datasets.

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Acknowledgments

We deeply acknowledge Taif University for Supporting this study through Taif University Researchers Supporting Project number (TURSP-2020/327), Taif University, Taif, Saudi Arabia.

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

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Authors and Affiliations

  1. Research Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS), 1173, 3038, Sfax, BP, Tunisia

    Emna Krichene & Adel M. Alimi

  2. Centre de recherche en numérique de Sfax, B.P. 275, Sakiet Ezzit, 3021, Sfax, Tunisia

    Wael Ouarda

  3. College of Business, Al Ain University of Science and Technology, Abu Dhabi, United Arab Emirates

    Habib Chabchoub

  4. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, 98071-2259, USA

    Ajith Abraham

  5. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O.Box. 11099, Taif, 21944, Saudi Arabia

    Abdulrahman M. Qahtani

  6. College of Applied Computer Science, King Saud University, Riyadh, Saudi Arabia

    Omar Almutiry & Habib Dhahri

  7. Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Adel M. Alimi

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  1. Emna Krichene
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Correspondence to Emna Krichene.

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Krichene, E., Ouarda, W., Chabchoub, H. et al. Taylor-based optimized recursive extended exponential smoothed neural networks forecasting method. Appl Intell 53, 7254–7277 (2023). https://doi.org/10.1007/s10489-022-03890-w

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