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Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Survey

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Innovations in Machine and Deep Learning

Part of the book series: Studies in Big Data ((SBD,volume 134))

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Abstract

Residual-feedback artificial neural networks are a type of artificial neural network (ANNs) that have shown better forecasting performance on some time series. One of the challenges of residual-feedback ANNs is by utilizing the previous time step’s observed value, they are only capable of predicting one step ahead in advance. Therefore, it would not be possible to apply them directly in a recursive multi-step forecast strategy. To shed light on this challenge, a systematic literature review was conducted in this paper to find answers to the following three research questions: What are the main motivations behind introducing residual feedback to ANNs? How good are the existing residual-feedback ANNs compared to other forecasting methods in terms of forecasting performance? And what are the existing solutions for recursive multi-step time series forecasting using residual-feedback ANNs? An analysis of 19 studies was conducted to answer these questions. Furthermore, several potential solutions that can be further practically explored are suggested in an attempt to overcome this challenge.

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Authors and Affiliations

  1. School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK

    Waddah Saeed

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, 86400, Johor, Malaysia

    Rozaida Ghazali

Authors
  1. Waddah Saeed
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  2. Rozaida Ghazali
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Correspondence to Waddah Saeed .

Editor information

Editors and Affiliations

  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Universidad Tecnológica de La Habana, La Habana, Cuba

    Alejandro Rosete

  3. School of Engineering, University of Cadiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Instituto Tecnológico de Ciudad Madero, Tecnológico Nacional de México, Tamaulipas, Mexico

    Nelson Rangel-Valdez

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Saeed, W., Ghazali, R. (2023). Recursive Multi-step Time-Series Forecasting for Residual-Feedback Artificial Neural Networks: A Survey. In: Rivera, G., Rosete, A., Dorronsoro, B., Rangel-Valdez, N. (eds) Innovations in Machine and Deep Learning. Studies in Big Data, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-031-40688-1_1

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