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Zaman Serisi Tahmini İçin Yeni Bir Yaklaşım: Bulanık Regresyon Ağı Fonksiyonları

Yıl 2025, Cilt: 37 Sayı: 1, 36 - 52, 25.03.2025

Mehmet Raci Aktoprak , Ozge Cagcag Yolcu

https://doi.org/10.7240/jeps.1573839

Öz

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.

Anahtar Kelimeler

Bulanık regresyon ağı fonksiyonları Yapay sinir ağları Tip-1 bulanık fonksiyonlar Zaman serisi tahmini

Etik Beyan

This study has been supported, by Marmara University Scientific Research Projects Coordinatorship, as part of the Master Science Thesis Projects (FYL-2022- 10538).

Destekleyen Kurum

Marmara University Scientific Research Projects Coordinatorship

Proje Numarası

FYL-2022- 10538

Teşekkür

This study has been supported, by Marmara University Scientific Research Projects Coordinatorship, as part of the Master Science Thesis Projects (FYL-2022- 10538).

Kaynakça

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

Yıl 2025, Cilt: 37 Sayı: 1, 36 - 52, 25.03.2025

Mehmet Raci Aktoprak , Ozge Cagcag Yolcu

https://doi.org/10.7240/jeps.1573839

Öz

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.

Anahtar Kelimeler

Fuzzy regression network functions Artificial neural networks Type-1 fuzzy functions Time series prediction

Etik Beyan

This study has been supported, by Marmara University Scientific Research Projects Coordinatorship, as part of the Master Science Thesis Projects (FYL-2022- 10538).

Destekleyen Kurum

Marmara University Scientific Research Projects Coordinatorship

Proje Numarası

FYL-2022- 10538

Teşekkür

This study has been supported, by Marmara University Scientific Research Projects Coordinatorship, as part of the Master Science Thesis Projects (FYL-2022- 10538).

Kaynakça

  • Q. Song, B.S. (1993). Chissom, Fuzzy time series and its models, Fuzzy Sets Syst 54. https://doi.org/10.1016/0165-0114(93)90372-O.
  • L.A. Zadeh, (1965). Fuzzy sets, Information and Control 8. https://doi.org/10.1016/S0019-9958(65)90241-X.
  • Q. Song, B.S. Chissom, (1993). Forecasting enrollments with fuzzy time series - Part I, Fuzzy Sets Syst 54. https://doi.org/10.1016/0165-0114(93)90355-L.
  • Q. Song, B.S. Chissom, (1994). Forecasting enrollments with fuzzy time series - part II, Fuzzy Sets Syst 62. https://doi.org/10.1016/0165-0114(94)90067-1.
  • S.M. Chen, (1996). Forecasting enrollments based on fuzzy time series, Fuzzy Sets Syst 81. https://doi.org/10.1016/0165-0114(95)00220-0.
  • S.M. Chen, (2002). Forecasting enrollments based on high-order fuzzy time series, Cybern Syst 33. https://doi.org/10.1080/019697202753306479.
  • K. Huarng, (2001). Effective lengths of intervals to improve forecasting in fuzzy time series, Fuzzy Sets Syst 123. https://doi.org/10.1016/S0165-0114(00)00057-9.
  • E. Egrioglu, C.H. Aladag, U. Yolcu, V.R. Uslu, M.A. Basaran, (2010). Finding an optimal interval length in high order fuzzy time series, Expert Syst Appl 37. https://doi.org/10.1016/j.eswa.2009.12.006.
  • E. Egrioglu, C.H. Aladag, M.A. Basaran, U. Yolcu, V.R. Uslu, (2011). A new approach based on the optimization of the length of intervals in fuzzy time series, Journal of Intelligent and Fuzzy Systems 22. https://doi.org/10.3233/IFS-2010-0470.
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Toplam 94 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Denetimli Öğrenme, Bulanık Hesaplama, Esnek Hesaplama
Bölüm Araştırma Makaleleri
Yazarlar

Mehmet Raci Aktoprak 0009-0007-5754-944X

Ozge Cagcag Yolcu 0000-0003-3339-9313

Proje Numarası FYL-2022- 10538
Erken Görünüm Tarihi 19 Mart 2025
Yayımlanma Tarihi 25 Mart 2025
Gönderilme Tarihi 25 Ekim 2024
Kabul Tarihi 17 Ocak 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 37 Sayı: 1

Kaynak Göster

APA Aktoprak, M. R., & Cagcag Yolcu, O. (2025). A New Approach for Time Series Prediction: Fuzzy Regression Network Functions. International Journal of Advances in Engineering and Pure Sciences, 37(1), 36-52. https://doi.org/10.7240/jeps.1573839
AMA Aktoprak MR, Cagcag Yolcu O. A New Approach for Time Series Prediction: Fuzzy Regression Network Functions. JEPS. Mart 2025;37(1):36-52. doi:10.7240/jeps.1573839
Chicago Aktoprak, Mehmet Raci, ve Ozge Cagcag Yolcu. “A New Approach for Time Series Prediction: Fuzzy Regression Network Functions”. International Journal of Advances in Engineering and Pure Sciences 37, sy. 1 (Mart 2025): 36-52. https://doi.org/10.7240/jeps.1573839.
EndNote Aktoprak MR, Cagcag Yolcu O (01 Mart 2025) A New Approach for Time Series Prediction: Fuzzy Regression Network Functions. International Journal of Advances in Engineering and Pure Sciences 37 1 36–52.
IEEE M. R. Aktoprak ve O. Cagcag Yolcu, “A New Approach for Time Series Prediction: Fuzzy Regression Network Functions”, JEPS, c. 37, sy. 1, ss. 36–52, 2025, doi: 10.7240/jeps.1573839.
ISNAD Aktoprak, Mehmet Raci - Cagcag Yolcu, Ozge. “A New Approach for Time Series Prediction: Fuzzy Regression Network Functions”. International Journal of Advances in Engineering and Pure Sciences 37/1 (Mart 2025), 36-52. https://doi.org/10.7240/jeps.1573839.
JAMA Aktoprak MR, Cagcag Yolcu O. A New Approach for Time Series Prediction: Fuzzy Regression Network Functions. JEPS. 2025;37:36–52.
MLA Aktoprak, Mehmet Raci ve Ozge Cagcag Yolcu. “A New Approach for Time Series Prediction: Fuzzy Regression Network Functions”. International Journal of Advances in Engineering and Pure Sciences, c. 37, sy. 1, 2025, ss. 36-52, doi:10.7240/jeps.1573839.
Vancouver Aktoprak MR, Cagcag Yolcu O. A New Approach for Time Series Prediction: Fuzzy Regression Network Functions. JEPS. 2025;37(1):36-52.
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