A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting

2012 ◽  
Vol 25 (2) ◽  
pp. 295-308 ◽  
Author(s):  
Chunshien Li ◽  
Jhao-Wun Hu
Keyword(s):  
Time Series ◽  
Swarm Intelligence ◽  
Time Series Forecasting ◽  
Fuzzy Approach ◽  
Neuro Fuzzy

A novel self-organizing complex neuro-fuzzy approach to the problem of time series forecasting

2013 ◽  
Vol 99 ◽  
pp. 467-476 ◽  
Author(s):  
Chunshien Li ◽  
Tai-Wei Chiang ◽  
Long-Ching Yeh
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Fuzzy Approach ◽  
Problem Of Time ◽  
Neuro Fuzzy ◽  
Self Organizing

A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction

2020 ◽  
Vol 268 ◽  
pp. 114977 ◽  
Author(s):  
Mohammed Ali Jallal ◽  
Aurora González-Vidal ◽  
Antonio F. Skarmeta ◽  
Samira Chabaa ◽  
Abdelouhab Zeroual
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Forecasting ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

scholarly journals Towards a Neuro-Fuzzy System for Time Series Forecasting in Maintenance Applications

2008 ◽  
Vol 41 (2) ◽  
pp. 12855-12860 ◽  
Author(s):  
M. El-Koujok ◽  
R. Gouriveau ◽  
N. Zerhouni
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Neuro Fuzzy

Neuro-fuzzy system for chaotic time series forecasting

1997 ◽  
Author(s):  
Francesco Masulli ◽  
Leonard Studer
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Chaotic Time Series ◽  
Neuro Fuzzy

scholarly journals ỨNG DỤNG ĐẠI SỐ GIA TỬ TRONG DỰ BÁO CHUỖI THỜI GIAN MỜ

2016 ◽  
Vol 54 (2) ◽  
pp. 161
Author(s):  
Nguyễn Cát Hồ ◽  
Nguyễn Công Điều ◽  
Vũ Như Lân
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Fuzzy Approach ◽  
University Of Alabama ◽  
Simple Arithmetic ◽  
Proposed Model ◽  
Improving Accuracy ◽  
The University ◽  
And Control

Fuzzy time series given by Song & Chissom (1993) in magazine "Fuzzy Sets and   Systems" has been widely studied for forecasting purposes. However, the accuracy of forecasts based on the concept of fuzzy approach of Song & Chissom is not high because of such depends on many factors. Chen (1996) proposed an efficient fuzzy time series model which consists of simple arithmetic calculations only. After that, this has been widely studied for improving accuracy of forecasting in many applications to get better results. The hedge algebras developed by Nguyen and Wechler (1990) was completely different from the fuzzy approach. Here the hedge algebras was used to model  linguistic domains and variables and their semantic structure is obtained. Instead of performing fuzzification and defuzzification, more simple methods are adopted, termed as semantization and desemantization, respectively. The hedge algebras based fuzzy system is a new topic, which was first applied to fuzzy control 2008 [16]. Hedge algebras applications for some specific problems in the field of information technology and control has a number of important results and confirm advantages of this approach in comparing with fuzzy approach. In continuilty of hedge algebras applications, this paper is mainly focused on the field of  fuzzy time series forecasting under hedge algebras approach. In this paper, we present a new approach using hedge algebras to provide a computational model, which is completely different from the fuzzy approach for fuzzy time series forecasting. The experimental results of forecasting enrollments of students of the University of Alabama show that the model of fuzzy time series based on hedge algebras is better than many existing models. We can see that the proposed model gains higher forecasting accuracy than the original model presented by Song and Chissom (1993b), Chen (1996, 2002), or Lee (2009), Qiu (2011), Egrioglu (2012), Ozdemir ( 2012) and Uslu (2013).

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