Adaptive Multidimensional Neuro-Fuzzy Inference System for Time Series Prediction

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

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Using adaptive neuro-fuzzy inference system for hydrological time series prediction

Applied Soft Computing ◽

10.1016/j.asoc.2007.07.011 ◽

2008 ◽

Vol 8 (2) ◽

pp. 928-936 ◽

Cited By ~ 99

Author(s):

Mohammad Zounemat-Kermani ◽

Mohammad Teshnehlab

Keyword(s):

Time Series ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Time Series Prediction ◽

Inference System ◽

Neuro Fuzzy

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Prediction of Electroencephalogram Time Series via Artificial Neuro-Fuzzy Inference System Trained by League Championship Algorithm

Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-4769-3.ch011 ◽

2018 ◽

pp. 232-248

Author(s):

Mehmet Mehdi Karakoc

Keyword(s):

Time Series ◽

Fuzzy Inference System ◽

Intelligent Systems ◽

Fuzzy Inference ◽

Time Series Prediction ◽

Inference System ◽

Neuro Fuzzy ◽

Electroencephalogram Eeg ◽

Analyze Time Series ◽

Over Time

Time series analysis has a wide application interest in artificial-intelligence-oriented research studies. Because it is easy to run machine-learning-based solutions directly over time series, it has been a popular approach to use alternative types of intelligent systems to analyze time series. Regarding such works, time series prediction is known as a remarkable topic as followed by researchers from different fields. The objective of this chapter is to provide an alternative work by using artificial neuro-fuzzy inference system trained by the league championship algorithm, which is an optimization algorithm from the associated literature. As the application objective, electroencephalogram (EEG) time series have been tried to be predicted by using the designed ANFIS-LCA approach. The chapter briefly introduces details about the approach and reports findings from the performed prediction operations.

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ANFIS Model for Time Series Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.1411 ◽

2013 ◽

Vol 385-386 ◽

pp. 1411-1414 ◽

Cited By ~ 4

Author(s):

Xue Bo Jin ◽

Jiang Feng Wang ◽

Hui Yan Zhang ◽

Li Hong Cao

Keyword(s):

Time Series ◽

Fuzzy Inference System ◽

Prediction Error ◽

Fuzzy Inference ◽

Time Series Prediction ◽

Prediction Performance ◽

Membership Functions ◽

Adaptive Network ◽

Anfis Model ◽

Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

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