KRİPTO PARA FİYATLARININ LSTM VE GRU MODELLERİ İLE TAHMİNİ

Yakın geçmişte hayatımıza giren ve kısa zamanda finansal piyasalarda kendisine yer bulan kripto paralar, hem bir değişim aracı hem de bir yatırım aracı olarak kullanılmaktadır. Kripto paraların merkezi bir otoritenin kontrolünde olmaması bu araçların fiyatlarında dalgalanmaları beraberinde getirmiştir. Bu nedenle, akıllı bir tahmin modelinin geliştirilmesi, yatırım yapılacak finansal varlıkların seçimi ve yatırım kararlarının hayata geçirilmesi açısından oldukça önemlidir. Derin öğrenme ve yapay zeka, yatırım yapılacak olan kripto para birimi ve diğer yatırım araçlarının seçiminde kullanılmaktadır. Tekrarlayan Sinir Ağı (RNN), Uzun-Kısa Süreli Bellek (LSTM) ve Geçitli Yinelenen Birim (GRU) modeli gibi derin öğrenme modellerinin, kripto para birimi fiyat tahmininde geleneksel zaman serisi modellerinden daha iyi performans gösterdiği araştırmacılar tarafından kanıtlanmıştır. Bundan dolayı bu çalışmada, özel bir RNN yöntemi olan LSTM ve GRU’dan yararlanılarak, günümüzde piyasa değeri ve işlem hacmi en yüksek olan kripto paralardan Bitcoin, Ethereum ve Ripple’ın 30 günlük fiyat tahmininde bulunulmuştur. Araştırmanın sonucunda her iki modelde de en iyi tahmin sonucunu Bitcoin vermiştir. İkinci en iyi tahmin sonucu Ripple, sonrasında ise Ethereum için bulunmuştur. Kullanılan yöntemler karşılaştırıldığında ise MAPE performans ölçütüne göre en iyi tahmin sonucuna Bitcoin ve Ripple için GRU, Ethereum için ise LSTM modeli ile ulaşılmıştır.

Anahtar Kelimeler:

Kripto Para, Bitcoin, Ethereum, Ripple, Derin Öğrenme, RNN, LSTM, GRU, Tahmin.

PREDICTION OF CRYPTO MONEY PRICES WITH LSTM AND GRU MODELS

Cryptocurrencies, which entered our lives in the recent past and found a place in the financial markets in a short time, are used both as a means of exchange and an investment tool. The fact that cryptocurrencies are not under the control of a central authority has brought about fluctuations in the prices of these vehicles. Therefore, the development of an intelligent forecasting model is very important for the selection of financial assets to be invested and the realization of investment decisions. Deep learning and artificial intelligence are used in the selection of cryptocurrency and other investment instruments to be invested. Deep learning models such as the Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) model have been proven by researchers to outperform traditional time series models in cryptocurrency price prediction. For this reason, in this study, a 30-day price estimate of Bitcoin, Ethereum and Ripple, which are the crypto currencies with the highest market value and transaction volume, has been made using LSTM and GRU, a special RNN method. As a result of the research, Bitcoin gave the best prediction result in both models. The second best prediction result was found for Ripple, then Ethereum. When the methods used were compared, the best estimation result was reached with the GRU model for Bitcoin and Ripple, and the LSTM model for Ethereum, according to the MAPE performance criterion.

Keywords:

Crypto-Currency, Bitcoin, Ethereum, Ripple, Deep Learning, RNN, LSTM, GRU, Predict.,

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