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Journals / Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi / 2025 / Cilt: 12 Sayı: 2

Comparison of Ensemble Learning Methods in Time Series Classification

Zaman Serisi Sınıflandırmasında Topluluk Öğrenme Yöntemlerinin Karşılaştırılması

Şeyda Aslan(MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ)0009-0006-2924-9098
Nevin Güler Dincer(MUGLA SITKI KOCMAN UNIVERSITY)0000-0003-0361-1803
Pages
653–670
Publication Date
2025-11-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In line with the specified purpose, 31 ensemble learning algorithms are applied to 100 UCR data sets.

📌 3 Key Takeaways

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Objective & Hypothesis:Time series classification (TSC) is a special type of classification that attempts to predict the relationship between time series, a sequence of values where each value is associated with a specific point in time, and a class variable.
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Key Findings & Data:The aim of this study is to compare the classification success of ensemble learning methods selected from different categories in TSC and to identify the method with the highest classification performance.
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Conclusion & Impact:In line with the specified purpose, 31 ensemble learning algorithms are applied to 100 UCR data sets.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Torbalama · Artırma · İstifleme

Abstract

Time series classification (TSC) is a special type of classification that attempts to predict the relationship between time series, a sequence of values where each value is associated with a specific point in time, and a class variable. TSC methods can be divided into three main categories as base, ensemble and deep learning methods according to the classification method, or raw data, feature and model based according to on the items used in the classification. This study focuses on using of ensemble learning methods in TSC. Ensemble learning methods combine the classification results obtained from more than one base learning methods or ensemble learning methods for the classification. Ensemble learning methods are divided into three categories: bagging, boosting, and stacking. The aim of this study is to compare the classification success of ensemble learning methods selected from different categories in TSC and to identify the method with the highest classification performance. In this context, this study is expected to provide guidance in determining the classification methods that should be focused on when developing new ensemble learning methods. In line with the specified purpose, 31 ensemble learning algorithms are applied to 100 UCR data sets.

Özet

Zaman serisi sınıflandırması (ZSS), her bir değerin zaman içinde belirli bir nokta ile ilişkilendirildiği bir değerler dizisi olan zaman serileri ile bir sınıf değişkeni arasındaki ilişkiyi tahmin etmeye çalışan özel bir sınıflandırma türüdür. ZSS yöntemleri, sınıflandırma yöntemine göre temel, topluluk ve derin öğrenme yöntemleri veya sınıflandırmada kullanılan öğelere göre ham veri, özellik ve model tabanlı olmak üzere üç ana kategoriye ayrılabilir. Bu çalışma, ZSS'de , topluluk öğrenme yöntemlerinin kullanımına odaklanmaktadır. Topluluk öğrenme yöntemleri, sınıflandırma için birden fazla temel öğrenme yönteminden veya topluluk öğrenme yönteminden elde edilen sınıflandırma sonuçlarını birleştirir. Topluluk öğrenme yöntemleri torbalama, artırma ve istifleme olmak üzere üç kategoriye ayrılır. Bu çalışmanın amacı, farklı kategorilerden seçilen topluluk öğrenme yöntemlerinin ZSS'deki sınıflandırma başarısını karşılaştırmak ve en yüksek sınıflandırma başarısına sahip yöntemi tespit etmeye çalışmaktır. Bu bağlamda, bu çalışmanın yeni topluluk öğrenme yöntemleri geliştirirken odaklanılması gereken sınıflandırma yöntemlerini belirlemede yol gösterici olacağı düşünülmektedir. Belirtilen amaç doğrultusunda, 31 topluluk öğrenme algoritması 100 UCR veri setine uygulanmıştır.

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Cite This Article

Aslan, Ş., Dincer, N. G. (2025). Comparison of Ensemble Learning Methods in Time Series Classification. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, Cilt: 12 Sayı: 2, 653–670. https://doi.org/10.35193/bseufbd.1565320

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