Finansal Zaman Serilerini Tahminlemede Kullanılan Yöntemlere Genel Bir Bakış

Year 2022, Volume: 9 Issue: 1, 653 - 671, 30.06.2022

Nuh Yurduseven , Ahmet Anıl Müngen

https://doi.org/10.35193/bseufbd.1087654

Cited By: 1

Abstract

Geçmişte olduğu gibi günümüzde de yatırımcılar için finansal verilerin trendinin tahmin edilebilmesi ve bu bilgi kullanılarak bir finansal strateji oluşturulması oldukça önemlidir. Fakat günümüzde hızlı internet bağlantıları ile finansal verilerin hızlı ulaşması ve bilişim ve bulut sistemlerindeki gelişmeler, finansal tahminlemek için yapay zekâ algoritmalarının kullanılması bu alanda rekabeti artırmaktadır. Fintech içinde portföy yönetimi gibi alanlarda yapay zekâ uygulamalarının kullanım payı gittikçe artmaktadır. Bu çalışmanın amacı finansal zaman serisi verileri tahminlemek için yapılan daha önceki akademik çalışmaları derlemek, zaman serilerinin tahmin etmek için kullanılan yapay zekâ algoritmalarını açıklamak ve tahmin edilen bazı finansal veri tiplerini ve bağımlılıklarını irdelemektir. Çalışma sonunda incelenen makalelerde kullanılan tekniklerin yeterlilikleri ve hangi veri tipi için hangi metodun daha başarılı sonuçlar verebileceği gibi çıkarımlar yapılmıştır.

Keywords

Finansal Zaman Serisi, Öngörü, Yapay Zekâ, Veri Madenciliği

A General Review of the Methods Used Financial Time Series Forecasting

Abstract

As in the past, it is very important for investors to be able to predict the trend of financial data and to create a financial strategy using this information. However, nowadays, rapid access to financial data with fast Internet connections, developments in informatics, and cloud systems, the use of artificial intelligence algorithms for financial forecasting increase competition in this field. The share of artificial intelligence applications in areas such as portfolio management in Fintech is increasing. The aim of this study is to compile previous academic studies to predict financial time series data, to explain artificial intelligence algorithms used to predict time series, and to examine some predicted financial data types and their dependencies. At the end of the study, inferences were made such as the adequacy of the techniques used in the articles examined and which method could yield more successful results for which data type.

Keywords

Financial Time Series, Forecasting, Artificial Intelligent, Data Mining

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