BİST 100 Endeksindeki Volatilitenin Zaman Serileri İle Analizi: Fbprophet ve LSTM Modeli Karşılaştırması

Öz

Geçmişteki verilere dayanarak gelecek hakkında tahminler yapmak analitik finanstaki en önemli konulardan birisidir. Son dönemde gelişen derin öğrenme yaklaşımları ve makine öğrenmesi modelleri bu alana olan ilgiyi arttırmıştır. Bu yaklaşımlardan birisi olan zaman serileri ile belirli frekanstaki değişimler tahmin edilmeye çalışılmaktadır. Bu çalışmada BIST-100 endeksine ait verileri tahmin edebilmek için LSTM (Long Short-Term Memory) ve Fbprophet (Facebook Prophet) yöntemleri kullanılmıştır. Düzensiz davranışlara sahip borsa endekslerinin tahmin edilmesi karmaşık bir iştir ancak geliştirilen yeni algoritmalar ile fiyat tahminleri daha öngörülebilir hale gelebilmektedir. Araştırma yüksek volatiliteye sahip 2021-01-01 ile 2021-12-31 arasındaki endeks verileri üzerinden gerçekleştirilmiştir. Kullandığımız modellerin değerlendirme kriterleri MAE (ortalama mutlak hata), MSE (ortalama kare hatası) ve RMSE (kök ortalama kare hatası)’dır. Çalışma sonucunda düşük hata oranları ile LSTM modelinin Fbprophet modelinden daha başarılı olduğu tespit edilmiştir.

Anahtar Kelimeler

• BİST 100 Endeksi
• Makine Öğrenmesi
• Derin Öğrenme
• LSTM
• Fbprophet.

Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model

Abstract

Making predictions about the future based on past datasets is one of the most important issues in analytical finance. Recently developed deep learning approaches and machine learning models have increased the interest in this field. One of these approaches, time series, is trying to predict the changes in a certain frequency. In this study, LSTM (Long Short-Term Memory) and Fbprophet (Facebook Prophet) methods were used to estimate the data of BIST-100 index. Predicting stock market indices with erratic behavior is a complex task, but with the new algorithms developed, price predictions can become more predictable. The research was carried out on the index data between 2021-01-01 and 2021-12-31, which has high volatility. The evaluation criteria of the models we used are MAE (mean absolute error), MSE (mean square error) and RMSE (root mean square error). As a result of the study, it was determined that the LSTM model was more successful than the Fbprophet model with a low error rates.

Keywords

• BIST 100 Index
• Machine Learning
• Deep Learning
• LSTM
• Fbprophet.

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