Transformer (Dönüştürücü) Mimarisi Kullanarak Zaman Serilerinde Anomali Tespiti

Çok değişkenli zaman verilerinde anormallik tespiti pek çok uygulama için önem arz etmektedir. Ancak, yüksek doğrulukla ve hızlı bir şekilde anormallik tespiti tam olarak çözülebilmiş bir problem değildir. Pek çok zaman verisi setindeki veri oynaklığı da problemi güçlendirmektedir. Son yıllarda anormallik tespiti için istatistiksel yaklaşımların yanısıra derin öğrenme tabanlı yöntemler ortaya çıkmıştır. Bunların içinde 2017 yılında ortaya çıkan ve ilk olarak dil çeviri uygulamalarında kullanılan dönüştürücü (transformer) tabanlı modeller önerilmiştir. Dönüştürücü tabanlı modeller paralel olarak işlem yaparak eğitim sürecini kısaltıp çeşitli uygulamalarda yüksek performans sergilemektedir. 2022 yılında çıkan TranAD isimli model hem temel modellere göre 100 kata kadar daha hızlı eğitim sürecine sahiptir hem de performans olarak pek çok modelden daha iyidir. Bu çalışmada ilgili model daha da hızlı eğitim yapacak şekilde modifiye edilmiştir ve orjinal makalede denenmeyen halka açık başka bir çok zamanlı verisetinde denenmiştir. Ortaya çıkan model eski bir dizüstü bilgisayarda yirmi saniyenin altında eğitilip, kendisinden daha fazla veri kullanan ve daha çok kaynak gerektiren modellere benzer performans sergilemiştir. Elde edilen model çeşitli zaman serilerinde, özellikle de sanayi 4.0 uygulamlarında kendine yer bulabilir.

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