TREND DETECTION THROUGH SEGMENTATION USING DISCRETE HIDDEN MARKOV MODEL

Yıl 2020, , 267 - 295, 29.06.2020

Özler Özgür

https://doi.org/10.17065/huniibf.530209

Öz

There are two models that benefit from the concept of hidden variables and are used frequently in practice. These are hidden Markov model and Markov switching model. Although there are quite a number of regime switching studies which applied the Markov switching model in the field of econometrics, studies that use the hidden Markov model for examining the regimes in econometric time series are sparse. In this study, we apply discrete hidden Markov model to four high-frequency time series and show that although transformed discrete data have to be used in the model structure, the model identifies two or more regimes quite well. It is concluded that the discrete hidden Markov model is defining regimes effectively, thereby, can also identify and segment trends.

Anahtar Kelimeler

Hidden Markov model, regime switching, trend detection, segmentation, pattern_finding

AYRIK GİZLİ MARKOV MODELİNİ KULLANARAK SEGMENTASYON YOLUYLA TREND TESPİTİ

Öz

Gizli değişkenler kavramından yararlanan ve pratikte sıklıkla kullanılan iki model bulunmaktadır. Bunlar gizli Markov modeli ve Markov rejim değişikliği modelidir. Ekonometri alanında Markov rejim değişikliği modelini uygulayan çok sayıda rejim değişikliği çalışması olmasına rağmen, ekonometrik zaman serilerindeki rejimleri incelemek için gizli Markov modelini kullanan çalışma sayısı çok azdır. Bu çalışmada, dört yüksek frekanslı zaman serisine ayrık gizli Markov modeli uygulanmış ve modellemede dönüştürülmüş ayrık veri kullanılmasına rağmen, gizli Markov modelinin iki ya da daha fazla rejimi oldukça iyi tanımladığı görülmüştür. Sonuç olarak ayrık gizli Markov modelinin rejimleri etkili bir şekilde tanımlayabildiği, böylece trendleri belirleyip seriyi segmentleyebildiği sonucuna varılmıştır.

Anahtar Kelimeler

Saklı Markov Modeli, rejim değişikliği, trend belirleme, segmentasyon, örüntü_tanıma

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