Abstract

Çalışmanın amacı, Bitcoin (BTC) fiyatlarının tahmininde yaygın olarak kullanılan iki zaman serisi yöntemini, Otoregresif Entegre Hareketli Ortalama (ARIMA) ve Çok Katmanlı Algılayıcı (MLP) Yapay Sinir Ağları (YSA), değerlendirmektir. Veri seti, 2020 ile 2022'nin ortası arasındaki dönemdeki haftalık BTC değerlerinden oluşmaktadır ve toplamda 135 gözlem içermektedir. Analizlerde R-Studio yazılımı kullanılmıştır. Verilerin durağanlığı, ADF, PP ve KPSS birim kök testleri ile kontrol edilmiştir. Tahminler için lineer bir yöntem olan ARIMA ve doğrusal olmayan bir yöntem olan MLP, YSA modeli kullanılmıştır. YSA modelinin daha iyi performans göstermesine rağmen, sınırlamalar nedeniyle kesin bir üstünlük belirtmek zordur. Her iki modelin de düşük performans göstermesinin olası nedeni, kripto varlıkların aşırı dalgalı ve spekülatif değerler taşıması ile iç yapılarından bağımsız hareket etme eğilimleridir.

Keywords

• Kripto varlıklar
• Bitcoin
• Zaman Serisi Tahmini
• ARIMA
• Sinir Ağı
• Çok Katmanlı Algılayıcı.

Bitcoin Crypto-Asset Prediction: With an Application of Linear Autoregressive Integrated Moving Average Method, and Non-Linear Multi-Layered and Feedback Artificial Neural Network Models

Abstract

The aim of the study is to evaluate two commonly used time series methods for forecasting Bitcoin (BTC) prices: the Autoregressive Integrated Moving Average (ARIMA) and the Multilayer Perceptron (MLP) Neural Network. The dataset consists of weekly BTC values from the period between 2020 and mid-2022, containing a total of 135 observations. The analyses were conducted using R-Studio software. The stationarity of the data was checked using ADF, PP, and KPSS unit root tests. For predictions, a linear method, ARIMA, and a nonlinear method, the MLP Neural Network model, were utilized. Although the MLP model demonstrated better performance, it is challenging to indicate a definitive superiority due to its limitations. The relatively low performance of both models may be attributed to the extreme volatility and speculative nature of cryptocurrencies, along with their tendency to behave independently of their underlying structures.

Keywords

• Crypto-assets
• Bitcoin
• Time Series Forecasting
• ARIMA
• Neural Network
• Multilayer Perceptron.

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