A New Approach for Time Series Prediction: Fuzzy Regression Network Functions

Mehmet Raci Aktoprak; Ozge Cagcag Yolcu

Makale detayları

A New Approach for Time Series Prediction: Fuzzy Regression Network Functions

Yazarlar:

Mehmet Raci Aktoprak | Ozge Cagcag Yolcu

Yayıncı:

Marmara University

Ana Başlıklar:

Denetimli Öğrenme | Bulanık Hesaplama | Esnek Hesaplama

Dergi:

International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 1, pp.36-52

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342789

pISSN:

2636-8277

Anahtar kelimeler:

Özet:

 The fuzzy regression functions FRFs constructs a comprehensive model by combining a series of linear functions based on the inputs. However, the relationship between input and output is not always purely linear. The approach presents novel FRFs with nonlinear structures based on neural networks, combining the strengths of both computational models and fuzzy logic. The proposed model generates membership values by fuzzifying real-valued time series observations, utilizing the fuzzy C-means clustering algorithm. Inputs are then created from the real-valued lagged observations and transformed membership values. A set of feed-forward neural networks, corresponding to the number of fuzzy sets, produces outputs as nonlinear functions of the inputs. These outputs are combined based on the membership values, representing the degree to which each time point belongs to the respective fuzzy sets, to generate the final predictions. The proposed prediction model is referred to as Fuzzy Regression Network Functions FRNFs. The prediction performance of FRNFs is investigated across several criteria by implementing it in various real-world time series datasets.

 The fuzzy regression functions FRFs constructs a comprehensive model by combining a series of linear functions based on the inputs. However, the relationship between input and output is not always purely linear. The approach presents novel FRFs with nonlinear structures based on neural networks, combining the strengths of both computational models and fuzzy logic. The proposed model generates membership values by fuzzifying real-valued time series observations, utilizing the fuzzy C-means clustering algorithm. Inputs are then created from the real-valued lagged observations and transformed membership values. A set of feed-forward neural networks, corresponding to the number of fuzzy sets, produces outputs as nonlinear functions of the inputs. These outputs are combined based on the membership values, representing the degree to which each time point belongs to the respective fuzzy sets, to generate the final predictions. The proposed prediction model is referred to as Fuzzy Regression Network Functions FRNFs. The prediction performance of FRNFs is investigated across several criteria by implementing it in various real-world time series datasets.

 The fuzzy regression functions FRFs constructs a comprehensive model by combining a series of linear functions based on the inputs. However, the relationship between input and output is not always purely linear. The approach presents novel FRFs with nonlinear structures based on neural networks, combining the strengths of both computational models and fuzzy logic. The proposed model generates membership values by fuzzifying real-valued time series observations, utilizing the fuzzy C-means clustering algorithm. Inputs are then created from the real-valued lagged observations and transformed membership values. A set of feed-forward neural networks, corresponding to the number of fuzzy sets, produces outputs as nonlinear functions of the inputs. These outputs are combined based on the membership values, representing the degree to which each time point belongs to the respective fuzzy sets, to generate the final predictions. The proposed prediction model is referred to as Fuzzy Regression Network Functions FRNFs. The prediction performance of FRNFs is investigated across several criteria by implementing it in various real-world time series datasets.

 The fuzzy regression functions FRFs constructs a comprehensive model by combining a series of linear functions based on the inputs. However, the relationship between input and output is not always purely linear. The approach presents novel FRFs with nonlinear structures based on neural networks, combining the strengths of both computational models and fuzzy logic. The proposed model generates membership values by fuzzifying real-valued time series observations, utilizing the fuzzy C-means clustering algorithm. Inputs are then created from the real-valued lagged observations and transformed membership values. A set of feed-forward neural networks, corresponding to the number of fuzzy sets, produces outputs as nonlinear functions of the inputs. These outputs are combined based on the membership values, representing the degree to which each time point belongs to the respective fuzzy sets, to generate the final predictions. The proposed prediction model is referred to as Fuzzy Regression Network Functions FRNFs. The prediction performance of FRNFs is investigated across several criteria by implementing it in various real-world time series datasets.

Makale detayları

A New Approach for Time Series Prediction: Fuzzy Regression Network Functions

Yazarlar:

Mehmet Raci Aktoprak | Ozge Cagcag Yolcu

Yayıncı:

Marmara University

Ana Başlıklar:

Denetimli Öğrenme | Bulanık Hesaplama | Esnek Hesaplama

Dergi:

International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 1, pp.36-52

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342789

pISSN:

2636-8277

Fuzzy regression network functions

Artificial neural networks

Type-1 fuzzy functions

Time series predictionBulanık regresyon ağı fonksiyonları

Yapay sinir ağları

Tip-1 bulanık fonksiyonlar

Zaman serisi tahmini

Sayı

Makale detayları

Yazarlar: Mehmet Raci Aktoprak | Ozge Cagcag Yolcu

Yayıncı: Marmara University

Ana Başlıklar: Denetimli Öğrenme | Bulanık Hesaplama | Esnek Hesaplama

Dergi: International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 1, pp.36-52

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001342789

pISSN: 2636-8277

Sayı

Yazarlar:

Mehmet Raci Aktoprak | Ozge Cagcag Yolcu

Yayıncı:

Marmara University

Ana Başlıklar:

Denetimli Öğrenme | Bulanık Hesaplama | Esnek Hesaplama

Dergi:

International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 1, pp.36-52

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342789

pISSN:

2636-8277