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Journals / International Periodical of Recent Technologies in Applied Engineering / 2026 / Cilt: 4

Forecasting Time Series: A Comparative Evaluation of Predictive Model

Tahmin Zaman Serisi: Öngörücü Modelin Karşılaştırmalı Değerlendirmesi

Murat Sakalıuzun(MARMARA UNIVERSITY)0009-0009-0051-8161
Buğra Kandemir0009-0000-9909-0808
Gökçen Gökyer Nalci0009-0008-3381-068X
Mustafa Nesin0009-0009-9065-5425
Dila Taşer Bedir0009-0001-3232-8532
Önder Demir(MARMARA UNIVERSITY)0000-0003-4540-663X
Kazım Yıldız(MARMARA UNIVERSITY)0000-0001-6999-1410
Pages
16–21
Publication Date
2026-08-31
DOI
—
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AI Summary & Key Takeaways

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

Furthermore, hybrid structures including ARFIMA-LSTM further enhance prediction accuracy by taking into account both linear and nonlinear components concurrently.

📌 3 Key Takeaways

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Objective & Hypothesis:In recent years, the increasing complexity of individual financial management processes and economic uncertainties, especially inflation, have increased the need for innovative modeling approaches in the field of personal expenditure forecasting and budget planning.
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Key Findings & Data:In this review, artificial intelligence (AI) and machine learning (ML) based methods developed in financial time series forecasting and expenditure forecasting were systematically examined.
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Conclusion & Impact:Furthermore, hybrid structures including ARFIMA-LSTM further enhance prediction accuracy by taking into account both linear and nonlinear components concurrently.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:zaman serileri · öncül · hibrit modeller

Abstract

In recent years, the increasing complexity of individual financial management processes and economic uncertainties, especially inflation, have increased the need for innovative modeling approaches in the field of personal expenditure forecasting and budget planning. In this review, artificial intelligence (AI) and machine learning (ML) based methods developed in financial time series forecasting and expenditure forecasting were systematically examined. Hybrid models such as ARIMA, Prophet, LSTM and ARFIMA-LSTM, which are widely used in literature, are compared in terms of their methodological features, advantages and limitations. As a result of the analysis, it has been seen that traditional models such as ARIMA are sufficient for short-term predictions in stationary and linear data, but their success in complex and nonlinear patterns is limited. On the other hand, LSTM and similar deep learning-based models have been shown to model long-term dependencies and complex fluctuations in financial time series more effectively. Furthermore, hybrid structures including ARFIMA-LSTM further enhance prediction accuracy by taking into account both linear and nonlinear components concurrently.

Özet

Son yıllarda, bireysel finansal yönetim süreçlerinin artan karmaşıklığı ve ekonomik belirsizlikler, özellikle enflasyon, kişisel harcama tahmini ve bütçe planlaması alanında yenilikçi modelleme yaklaşımlarına olan ihtiyacı artırmıştır. Bu incelemede, finansal zaman serisi tahmini ve harcama tahmininde geliştirilen yapay zeka (AI) ve makine öğrenimi (ML) tabanlı yöntemler sistematik olarak incelenmiştir. Literatürde yaygın olarak kullanılan ARIMA, Prophet, LSTM ve ARFIMA-LSTM gibi hibrit modeller, metodolojik özellikleri, avantajları ve sınırlamaları açısından karşılaştırılır. Analiz sonucunda, ARIMA gibi geleneksel modellerin sabit ve doğrusal verilerde kısa vadeli tahminler için yeterli olduğu, ancak karmaşık ve doğrusal olmayan desenlerdeki başarılarının sınırlı olduğu görülmüştür. Öte yandan, LSTM ve benzeri derin öğrenme tabanlı modellerin, finansal zaman serilerindeki uzun vadeli bağımlılıkları ve karmaşık dalgalanmaları daha etkili modellediği gösterilmiştir. Ayrıca, ARFIMA-LSTM dahil hibrit yapılar, hem doğrusal hem de doğrusal olmayan bileşenleri eşzamanlı olarak dikkate alarak tahmin doğruluğunu daha da artırır.

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Cite This Article

Sakalıuzun, M., Kandemir, B., Nalci, G. G., Nesin, M., Bedir, D. T., Demir, Ö., Yıldız, K. (2026). Forecasting Time Series: A Comparative Evaluation of Predictive Model. International Periodical of Recent Technologies in Applied Engineering, Cilt: 4, 16–21. https://ajindex.com/articles/1589384

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