Sunspot Time Series Prediction Using Parallel-Structure Fuzzy System

Author(s):  
Min-Soo Kim ◽  
Chan-Soo Chung
Author(s):  
CATHERINE VAIRAPPAN ◽  
SHANGCE GAO ◽  
ZHENG TANG ◽  
HIROKI TAMURA

A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.


Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain

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.


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