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Journals / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Sayı: 62

EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

DOW JONES ENDEKSİNİN İLERİ ZAMAN SERİSİ ANALİZİ: CEEMDAN AYRIŞTIRMASI KULLANILARAK YAPILAN KAPSAMLI BİR ÇALIŞMA

Ahmet Akusta(KONYA TEKNİK ÜNİVERSİTESİ)0000-0002-5160-3210
Pages
19–35
Publication Date
2024-05-16
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AI Summary & Key Takeaways

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

However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model.
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Key Findings & Data:The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices.
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Conclusion & Impact:However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Finansal Zaman Serisi Ayrıştırması · ARIMAX Modelleme · Finansal Piyasa Tahmini

Abstract

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

Özet

Bu çalışma, Tam Topluluk Ampirik Mod Ayrıştırması (CEEMDAN) ile Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) modelini entegre ederek yenilikçi bir finansal zaman serisi analizi yaklaşımı sunmaktadır. Araştırmanın birincil katkısı, büyük hisse senedi endekslerini yöneten dinamiklerin tahmin doğruluğunu ve anlaşılmasını arttırmaktır. Daha önce bu alanda kapsamlı bir şekilde kullanılmayan CEEMDAN, karmaşık finansal zaman serilerini uyarlamalı olarak içsel mod fonksiyonlarına (IMF'ler) ayrıştırmak için yenilikçi bir şekilde uygulanmıştır. CEEMDAN'ın karmaşık finansal zaman serilerini uyarlanabilir bir şekilde IMF'lere ayrıştırma yeteneği, ARIMAX'ın tarihsel eğilimlerin ve dış faktörlerin etkisini hesaba katan tahmin yeterliliği ile birleştirilmiştir. Metodoloji, çeşitli büyük ABD hisse senedi endekslerini dışsal değişkenler olarak içeren kapsamlı Dow Jones Endüstriyel Ortalama (DJIA) analizi ile doğrulanmıştır. Çalışmamız, literatürdeki yüksek performanslı modellerle uyumlu olarak 0,93'lük bir R² skoru sunmaktadır. Bununla birlikte, modelimizin benzersiz gücü, DJIA'nın gecikmesiz tahmininde yatmaktadır. Endeksin volatilitesini ve önemli hareketlerini yüksek doğrulukla yansıtarak finansal uygulamalar için son derece pratik hale getirmektedir.

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Cite This Article

Akusta, A. (2024). EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Sayı: 62, 19–35. https://doi.org/10.30794/pausbed.1398790

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