Aralıklı tip-2 bulanık c-ortalama ile zaman serilerinin bulanık dilsel özetlemesi: BIST100 örnek hisse uygulaması

Öz

Son yıllarda büyük ilgi gören dilsel özetleme, büyük verilerden insanların anlayabileceği çıkarımlar sağlamaktadır. Dilsel özetlemenin önemli bir aşaması, verilerin ne kadar doğru yansıtıldığını gösteren doğruluk derecesinin belirlenmesidir. Bu doğruluk derecelerinin hesaplanmasında literatürde eşit bölümleme yöntemi kullanılmaktadır fakat bu yöntem verilerin yoğunlaştığı aralıkları göz ardı etmektedir. Bu çalışmada, verilerin yoğun olarak yer aldığı aralıkları dikkate alarak bulanık kümeleri dağıtan Aralıklı Tip-2 Bulanık C Ortalama (AT2BCO) bölümleme yöntemi önerilmektedir. Önerilen yöntem ile aralıklı tip-2 bulanık kümeler oluşturulmuş, bulanık kardinalite tabanlı olasılık ve olabilirlik temelli yaklaşımlar kullanılarak dilsel özetlerin doğruluk dereceleri hesaplanmıştır. Önerilen yaklaşım adım adım açıklanmış ve literatürde kullanılan eşit bölümleme yöntemi ile sonuçları karşılaştırılmıştır. Önerilen AT2BCO bölümleme yöntemi, Borsa İstanbul'da (BIST) işlem gören üç hisse senedine ait son on yıllık dönemi kapsayan finansal zaman serilerine uygulanmıştır. Elde edilen sonuçlar, literatürde yer alan eşit bölümleme çalışmalarına göre daha kararlı sonuçlar ürettiğini göstermektedir.

Anahtar Kelimeler

• Dilsel Özetleme
• Aralıklı Tip-2 Bulanık C-Ortalama
• Veri Madenciliği
• Zaman Serileri

Fuzzy linguistic summarization of time series with interval type-2 Fuzzy c-means: BIST100 sample stock application

Öz

Linguistic summarization, which has garnered significant attention in recent years, facilitates the derivation of human-understandable insights from vast amounts of data. One critical aspect of linguistic summarization involves determining the truth degree that reflects the extent to which the underlying data has been appropriately represented. In the extant literature, the numerical values of fuzzy sets employed to calculate truth degrees have been generated using the uniform partitioning method, which neglects the intervals of data concentration. To address this limitation, the present study proposes the Interval Type-2 Fuzzy C-Means (IT2FCM) partitioning method, which distributes fuzzy sets, taking into account the intervals of data density. By using the proposed method, interval type-2 fuzzy sets are created, and the truth degrees of linguistic summaries are calculated using fuzzy cardinality-based probability and possibility based approaches. The proposed approach is explicated step by step, and its outcomes are compared to those of the uniform partitioning method used in prior research. To evaluate the efficacy of the proposed IT2FCM partitioning approach, we apply it to financial time series covering the last decade of three stocks traded on the Borsa İstanbul (BIST). The findings indicate that the proposed IT2FCM partitioning method generates more stable outcomes compared to the uniform partitioning studies reported in the literature.

Anahtar Kelimeler

• Linguistic Summarization
• Interval Type-2 Fuzzy C-Means
• Data Mining
• Time Series

Kaynakça

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