BEYOND EUCLIDEAN: A METRIC-OPTIMIZED TYPE-1 FUZZY SVR FUNCTIONS ARCHITECTURE FOR TIME SERIES FORECASTING

İbrahim Cimşit; Ali Zafer Dalar; Ufuk Yolcu

doi:10.37880/cumuiibf.1885704

EN TR

BEYOND EUCLIDEAN: A METRIC-OPTIMIZED TYPE-1 FUZZY SVR FUNCTIONS ARCHITECTURE FOR TIME SERIES FORECASTING

Abstract

This paper introduces a distance-aware forecasting framework based on type-1 fuzzy support vector regression functions, designed to examine the impact of distance metric selection on fuzzy time series prediction performance. The proposed approach extends fuzzification mechanisms by incorporating nine alternative distance functions into a fuzzy clustering-driven regression structure. These distance measures determine the formation of fuzzy partitions and directly influence the weighting scheme used in cluster-specific regression models. A unified optimization strategy is employed to jointly tune both fuzzification and regression components using a grid search procedure. This integrated optimization allows the framework to dynamically adapt to varying data distributions, temporal dependencies, and structural characteristics of time series data. The proposed method is validated through extensive experiments conducted on four real-world datasets from production and financial domains. The empirical findings reveal that distance metric choice significantly affects forecasting accuracy. In many experimental scenarios, non-Euclidean distance measures outperform classical Euclidean-based metrics. Distance functions emphasizing directional similarity, scale normalization, or maximum deviation frequently produce superior results. Chebyshev, standardized Euclidean, and Cosine distances achieve the lowest prediction errors across several datasets and evaluation periods. The results indicate that no single distance metric is universally optimal, underscoring the importance of metric flexibility and data-aware selection. The study demonstrates that integrating distance metric optimization into type-1 fuzzy support vector regression function-based architectures leads to consistent improvements in forecasting accuracy. The proposed architecture offers a generalizable solution for time series forecasting.

Keywords

ÖKLİDİN ÖTESİNDE: ZAMAN SERİSİ ÖNGÖRÜSÜ İÇİN METRİK-OPTİMİZE TİP-1 BULANIK DVR FONKSİYONLARI MİMARİSİ

Öz

Bu çalışma, bulanık zaman serisi tahmin performansı üzerinde uzaklık metriği seçiminin etkisini incelemek amacıyla, tip-1 bulanık destek vektör regresyon fonksiyonlarına dayalı, uzaklık metriklerine duyarlı bir tahmin çerçevesi önermektedir. Önerilen yaklaşım, bulanıklaştırma mekanizmalarını genişleterek, dokuz farklı uzaklık fonksiyonunu bulanık kümeleme temelli bir regresyon yapısına entegre etmektedir. Bu uzaklık metrikleri, bulanık bölümlerin oluşumunu belirlemekte ve kümeye özgü regresyon modellerinde kullanılan ağırlıklandırma şemasını doğrudan etkilemektedir. Bulanıklaştırma ve regresyon bileşenlerinin birlikte eniyilenmesi amacıyla, ızgara araması temelli birleşik bir optimizasyon stratejisi uygulanmıştır. Bu bütünleşik optimizasyon süreci, önerilen yapının farklı veri dağılımlarına, zamansal bağımlılıklara ve zaman serisi verilerinin yapısal özelliklerine dinamik biçimde uyum sağlamasına olanak tanımaktadır. Önerilen yöntem, üretim ve finans alanlarını temsil eden dört farklı zamansal davranış özelliklerine sahip gerçek hayat veri setleri üzerinde gerçekleştirilerek doğrulanmıştır. Bulgular, uzaklık metriği seçiminin tahmin doğruluğu üzerinde belirleyici bir etkiye sahip olduğunu ortaya koymaktadır. Birçok senaryoda, Öklid dışındaki uzaklık metriklerinin, Öklid tabanlı metriklere kıyasla daha başarılı sonuçlar ürettiği gözlemlenmiştir. Yönlülüğe duyarlı, ölçek normalizasyonunu dikkate alan ve maksimum sapmayı vurgulayan uzaklık fonksiyonları daha düşük tahmin hataları sağlamaktadır. Çebişev, standartlaştırılmış Öklid ve Kosinüs uzaklıkları, farklı veri kümeleri ve değerlendirme dönemleri boyunca en düşük tahmin hatalarını elde etmiştir. Elde edilen sonuçlar, tek bir uzaklık metriğinin tüm durumlar için evrensel olarak en iyi seçenek olmadığını göstermekte ve metrik esnekliği ile veri odaklı metrik seçiminin önemini ortaya koymaktadır. Çalışma, uzaklık metriği optimizasyonunun tip-1 bulanık destek vektör regresyon fonksiyonları tabanlı mimarilere entegre edilmesinin tahmin doğruluğunda tutarlı iyileşmeler sağladığını göstermekte ve zaman serisi tahmini için genellenebilir bir çözüm sunmaktadır.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Zaman Serileri Analizi

Bölüm

Araştırma Makalesi

Yazarlar

İbrahim Cimşit ^*
0000-0002-7345-8093
Türkiye

Ali Zafer Dalar
0000-0002-8574-461X
Türkiye

Ufuk Yolcu
0000-0002-0172-3353
Türkiye

Yayımlanma Tarihi

26 Nisan 2026

Gönderilme Tarihi

10 Şubat 2026

Kabul Tarihi

13 Nisan 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 27 Sayı: 2

DOI

https://doi.org/10.37880/cumuiibf.1885704

IZ

https://izlik.org/JA92PN43BL

Kaynak Göster

RIS / Bibtex

APA

Cimşit, İ., Dalar, A. Z., & Yolcu, U. (2026). BEYOND EUCLIDEAN: A METRIC-OPTIMIZED TYPE-1 FUZZY SVR FUNCTIONS ARCHITECTURE FOR TIME SERIES FORECASTING. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 27(2), 639-672. https://doi.org/10.37880/cumuiibf.1885704