Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction

Goudarzi, Sobhan; Khodabakhshi, Mohammad Bagher; Moradi, Mohammad Hassan

doi:10.3233/ifs-151839

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…Aladag et al (2014) analyzed the Australian beer consumption data set with the T1FRF method and compared the performance of the T1FRF method with some forecasting methods. Goudarzi et al (2016) proposed a novel interactively recurrent fuzzy functions for nonlinear chaotic time series prediction Aladag et al (2016) proposed a fuzzy time series forecasting method based on the fuzzy function approach that uses binary particle swarm optimization to determine the lagged variables of the system. Baser and Demirhan (2017) estimated the yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with a support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression

PİŞKİN

Baş

2022

Karadeniz Fen Bilimleri Dergisi

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Artificial neural networks, fuzzy inference systems, and hybrid methods where these methods are used together have been frequently used in forecasting problems. Although fuzzy inference systems produce very effective results in forecasting problems, the fact that many classical fuzzy inference systems depend on the rule base makes it difficult to implement these methods. The type 1 fuzzy regression functions approach, which is not dependent on the rule base and has a simpler structure than many fuzzy inference systems, is frequently used in forecasting problems. Although the Type 1 fuzzy regression functions approach has superior forecasting performance, it is known that the method has a multicollinearity problem in the application process of this method. The type 1 fuzzy regression functions approach based on ridge regression both eliminate the multicollinearity problem of the Type 1 fuzzy regression functions approach and produces better forecasting results than the Type 1 fuzzy regression functions approach. In this study, the forecasting of monthly house sales to foreigners is carried out for the first time with the Type 1 fuzzy regression functions approach based on ridge regression, and the results of the analysis are compared with many methods suggested in the literature for the forecasting problem. As a result of the analysis, it is concluded that the forecasting results obtained with the Type 1 fuzzy regression functions approach based on ridge regression produce better results than some other methods in the literature.

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Section: Introductionmentioning

confidence: 99%

Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression

PİŞKİN

Baş

2022

Karadeniz Fen Bilimleri Dergisi

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“…Barak and Sadegh (2016) used ensemble ARIMA-ANFIS hybrid algorithm for forecasting of energy consumption. Goudarzi et al (2016) proposed an interactively recurrent fuzzy function with multi-objective learning. proposed a type 1 fuzzy time series function method based on binary particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic fuzzy time series functions approach for time series forecasting

2020

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Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods.

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“…The proposed model had a worthier performance in convergence rate and forecasting accuracy than the self-constructing FNN. The authors of [117] presented the interactively recurrent fuzzy functions model for predicting the time series data of Lorenz, Mackey-Glass, and real-time lung sound signal modeling. The benchmark and real-time models' results showed to be better than the recurrent networks, such as fuzzy WNN, self-evolving FNN, ESN, and LS.…”

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confidence: 99%

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Ramadevi

Bingi

2022

Symmetry

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Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

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Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction

Cited by 12 publications

References 41 publications

Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression

Forecasting Monthly Housing Sales to Foreigners with Type 1 Fuzzy Regression Functions Approach Based on Ridge Regression

Intuitionistic fuzzy time series functions approach for time series forecasting

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

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