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All Journal JOIV : International Journal on Informatics Visualization
Haseeb Ali
Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Computer Science & IT

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Neural Network Techniques for Time Series Prediction: A Review Muhammad Faheem Mushtaq; Urooj Akram; Muhammad Aamir; Haseeb Ali; Muhammad Zulqarnain
JOIV : International Journal on Informatics Visualization Vol 3, No 3 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (902.684 KB) | DOI: 10.30630/joiv.3.3.281

Abstract

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.
Co-Authors Muhammad Aamir Muhammad Faheem Mushtaq Muhammad Zulqarnain Urooj Akram