Home > Research > Publications & Outputs > A new hybrid method for time series forecasting

Research

Links

Text available via DOI:

Keywords

View graph of relations

A new hybrid method for time series forecasting: AR–ANFIS

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

A new hybrid method for time series forecasting: AR–ANFIS. / Sarıca, Busenur; Eğrioğlu, Erol; Aşıkgil, Barış.
In: Neural Computing and Applications, Vol. 29, No. 3, 01.02.2018, p. 749-760.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Sarıca, B, Eğrioğlu, E & Aşıkgil, B 2018, 'A new hybrid method for time series forecasting: AR–ANFIS', Neural Computing and Applications, vol. 29, no. 3, pp. 749-760. https://doi.org/10.1007/s00521-016-2475-5

APA

Sarıca, B., Eğrioğlu, E., & Aşıkgil, B. (2018). A new hybrid method for time series forecasting: AR–ANFIS. Neural Computing and Applications, 29(3), 749-760. https://doi.org/10.1007/s00521-016-2475-5

Vancouver

Sarıca B, Eğrioğlu E, Aşıkgil B. A new hybrid method for time series forecasting: AR–ANFIS. Neural Computing and Applications. 2018 Feb 1;29(3):749-760. doi: 10.1007/s00521-016-2475-5

Author

Sarıca, Busenur ; Eğrioğlu, Erol ; Aşıkgil, Barış. / A new hybrid method for time series forecasting : AR–ANFIS. In: Neural Computing and Applications. 2018 ; Vol. 29, No. 3. pp. 749-760.

Bibtex

@article{5cd31a4d233d445589cac389c5f3be8c,
title = "A new hybrid method for time series forecasting: AR–ANFIS",
abstract = "In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.",
keywords = "Adaptive network fuzzy inference system, Autoregressive model, Fuzzy C-Means, Fuzzy inference system, Particle swarm optimization, Time series",
author = "Busenur Sarıca and Erol Eğrioğlu and Barı{\c s} A{\c s}ıkgil",
year = "2018",
month = feb,
day = "1",
doi = "10.1007/s00521-016-2475-5",
language = "English",
volume = "29",
pages = "749--760",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",
number = "3",

}

RIS

TY - JOUR

T1 - A new hybrid method for time series forecasting

T2 - AR–ANFIS

AU - Sarıca, Busenur

AU - Eğrioğlu, Erol

AU - Aşıkgil, Barış

PY - 2018/2/1

Y1 - 2018/2/1

N2 - In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

AB - In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

KW - Adaptive network fuzzy inference system

KW - Autoregressive model

KW - Fuzzy C-Means

KW - Fuzzy inference system

KW - Particle swarm optimization

KW - Time series

U2 - 10.1007/s00521-016-2475-5

DO - 10.1007/s00521-016-2475-5

M3 - Journal article

AN - SCOPUS:84979295867

VL - 29

SP - 749

EP - 760

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

IS - 3

ER -