Comparative Analysis of Deep Learning Models for Silver Price Prediction: CNN, LSTM, GRU and Hybrid Approach

Öz

In this study, the performance of different deep learning algorithms to predict silver prices was evaluated. It was focused on the use of deep learning models such as CNN, LSTM, and GRU for the prediction process, as well as a new hybrid model based on combining these models. Each algorithm was trained on historical silver price data and compared its performance in price prediction using this data. This approach aims to achieve more comprehensive and accurate forecasts by combining the strengths of each model. It also makes a unique contribution to the literature in this area by addressing a specialized area such as the silver market, which is often neglected in financial forecasting. The study presents an innovative approach to financial forecasting and analysis methodologies, highlighting the advantages and potential of deep learning models for time-series data processing. The results compare the ability of these algorithms to analyze silver prices based on historical data only and to assess past trends. The study showed that these algorithms exhibit different performances in analyzing historical data. In conclusion, this study compared the performance of different deep learning algorithms for predicting silver prices based on historical data and found that the CNN-LSTM-GRU hybrid model has the potential to make better predictions. These results can provide guidance to researchers working on financial analysis and forecasting.

Anahtar Kelimeler

• Silver Price Prediction
• Deep Learning
• LSTM
• CNN-LSTM-GRU
• Financial Analysis

Derin Öğrenme Modellerinin Gümüş Fiyat Tahmininde Karşılaştırmalı Analizi: CNN, LSTM, GRU ve Hibrit Yaklaşım

Öz

Bu çalışmada, gümüş fiyatlarını tahmin etmek amacıyla farklı derin öğrenme algoritmalarının performansını değerlendirilmiştir. Tahmin işlemi için CNN, LSTM ve GRU gibi derin öğrenme modellerinin kullanımı ile birlikte bu modellerin birleştirilmesi üzerine yeni bir hibrit model üzerine odaklanılmıştır. Her bir algoritma, geçmiş gümüş fiyat verileri üzerinde eğitilmiş ve bu verileri kullanarak fiyat tahminlerindeki performansları karşılaştırılmıştır. Bu yaklaşım, her bir modelin güçlü yönlerini birleştirerek daha kapsamlı ve hassas tahminler elde etmeyi hedefler. Ayrıca, finansal tahminlerde sıklıkla göz ardı edilen gümüş piyasası gibi özel bir alanı ele alarak, bu alandaki literatüre özgün bir katkı sağlamaktadır. Çalışma, zaman serisi verilerinin işlenmesi konusunda derin öğrenme modellerinin avantajlarını ve potansiyelini vurgulayarak, finansal tahmin ve analiz metodolojilerinde yenilikçi bir yaklaşım sunmaktadır. Sonuçlar, bu algoritmaların sadece geçmiş verilere dayalı olarak gümüş fiyatlarını analiz etme ve geçmiş trendleri değerlendirme yeteneklerini karşılaştırmıştır. Çalışma, bu algoritmaların geçmiş verilere dayalı analizlerde farklı performanslar sergilediğini göstermiştir. Sonuç olarak, bu çalışma, gümüş fiyatlarının geçmiş verileri üzerinden tahmin edilmesi için farklı derin öğrenme algoritmalarının performansını karşılaştırmış ve CNN-LSTM-GRU hibrit modelinin daha iyi tahminler yapma potansiyeli taşıdığını ortaya koymuştur. Bu sonuçlar, finansal analiz ve tahmin konularında çalışan araştırmacılara yol gösterici olabilir.

Anahtar Kelimeler

• Gümüş Fiyat Tahmini
• Derin Öğrenme
• LSTM
• CNN-LSTM-GRU
• Finansal Analiz

Etik Beyan

Bu çalışma için etik kurul iznine ihtiyaç duyulmamıştır.

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