TY  - JOUR
T1  - Stock Price Forecasting Using Machine Learning and Deep Learning Algorithms: A Case Study for the Aviation Industry
TT  - Makine Öğrenimi ve Derin Öğrenme Algoritmalarını Kullanarak Hisse Senedi Fiyat Tahmini: Havacılık Sektörüne Yönelik Bir Örnek Çalışma
AU  - Gür, Yunus Emre
PY  - 2024
DA  - March
DO  - 10.35234/fumbd.1357613
JF  - Fırat Üniversitesi Mühendislik Bilimleri Dergisi
PB  - Fırat Üniversitesi
WT  - DergiPark
SN  - 1308-9072
SP  - 25
EP  - 34
VL  - 36
IS  - 1
LA  - en
AB  - With technological advances, humans are constantly generating data through various electronic devices and sensors, and this data is stored in digital environments. A vast amount of data has served as a valuable asset that has facilitated the rise and progression of novel fields, including data science, artificial intelligence (AI), deep learning (DL), and the internet of things (IoT). Effectively managing and analyzing data provides a competitive advantage for modern businesses. The objective of this study is to forecast the stock price of Turkish Airlines (THY), a publicly traded corporation listed on Borsa Istanbul. In order to achieve the intended objective, the utilization of machine learning approaches like SVM and XGBoost, as well as the deep learning algorithm Long Short-Term Memory (LSTM), are used. The models are trained over a time period including daily data from January 4, 2010 to September 5, 2023. The forecast performance of the models is evaluated by comparing the actual and predicted stock prices and the model with the lowest error is identified. The proposed models' performances are assessed using the RMSE, MSE, MAE, and R2 error statistics. According to the results obtained, it is determined that the LSTM model has lower error coefficients than SVM and XGBoost models and gives the best performance.
KW  - LSTM
KW  - stock price prediction
KW  - machine learning
KW  - deep learning
KW  - SVM
KW  - XGBoost
N2  - Teknolojik ilerlemelerle birlikte, insanlar çeşitli elektronik cihazlar ve sensörler aracılığıyla sürekli olarak veri üretmekte ve bu veriler dijital ortamlarda depolanmaktadır. Bu büyük veri havuzu, yeni disiplinlerin doğmasına ve gelişmesine olanak tanıyan bir kaynak haline gelmiş; örneğin, veri bilimi, yapay zekâ, derin öğrenme ve nesnelerin interneti gibi alanlar ortaya çıkmıştır. Verilerin etkili bir şekilde yönetilmesi ve analiz edilmesi, modern işletmeler için rekabet avantajı sağlamaktadır. Bu çalışma, Borsa İstanbul'da (BIST) işlem gören Türk Hava Yolları AO (THYAO) şirketinin hisse senedi fiyatının tahmin edilmesini amaçlamaktadır. Bu amaçla, makine öğrenmesi algoritmalarından Support Vector Machine (SVM) ve Extreme Gradient Boosting (XGBoost) ile derin öğrenme algoritması olan Long Short-Term Memory (LSTM) kullanılmıştır. Modeller, 4 Ocak 2010 ile 5 Eylül 2023 tarihleri arasındaki günlük verileri içeren bir zaman diliminde eğitilmiştir. Gerçek hisse senedi fiyatları ile tahmin edilen fiyatlar karşılaştırılarak modellerin performansları değerlendirilmiş ve en düşük hataya sahip model belirlenmiştir. Önerilen modellerin performansları RMSE, MSE, MAE ve R2 hata istatistikleri kullanılarak değerlendirilmiştir. Elde edilen sonuçlara göre LSTM modelinin SVM ve XGBoost modellerine göre daha düşük hata katsayılarına sahip olduğu ve en iyi performansı verdiği belirlenmiştir.
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UR  - https://doi.org/10.35234/fumbd.1357613
L1  - https://dergipark.org.tr/tr/download/article-file/3396115
ER  -