İşletme Fakültesi

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Browsing İşletme Fakültesi by Journal "2018 3rd International Conference on Computer Science and Engineering (UBMK)"

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    Conference Object
    Citation - WoS: 51
    Citation - Scopus: 92
    Bitcoin Forecasting Using Arima and Prophet
    (IEEE, 2018) Yenidoğan, Işıl; Çayır, Aykut; Kozan, Ozan; Dağ, Tugce; Arslan, Çiğdem
    This paper presents all studies methodology and results about Bitcoin forecasting with PROPHET and ARIMA methods using R analytics platform. To find the most accurate forecast model the performance metrics of PROPHET and AMNIA methods are compared on the same dataset. The dataset selected 16r this study starts from May 2016 and ends in March 2018 which is the interval that Bitcoin values changing significantly against the other currencies. Data is prepared for time series analysis by performing data preprocessing steps such as time stamp conversion and feature selection. Although the time series analysis has a univariate characteristics it is aimed to include some additional variables to each model to improve the forecasting accuracy. Those additional variables are selected based on different correlation studies between cryptocurrencies and real currencies. The model selection for both ARIMA and PROPHET is done by using threefold splitting technique considering the time series characteristics of the dataset. The threefold splitting technique gave the optimum ratios for training validation and test sets. Filially two different models are created and compared in terms of performance metrics. Based on the extensive testing we see that PROPHET outperforms ARIMA by 0.94 to 0.68 in R-2 values.
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    Citation - WoS: 37
    Citation - Scopus: 52
    Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods
    (Institute of Electrical and Electronics Engineers Inc., 2018) Çayır, Aykut; Yenidoğan, Işıl; Dağ, Hasan
    Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper, we propose a method, which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms, such as, support vector machine, gradient boosting machines, and random forest. All of the proposed hybrid models and the above mentioned machine learning algorithms are trained on three different datasets: MNIST, Fashion-MNIST, and CIFAR-10. Results show that the proposed hybrid models are more successful than traditional models while they are being trained from raw pixel values. In this study, we empower traditional machine learning algorithms for classification using feature extraction ability of deep neural network architectures and we are inspired by transfer learning methodology to this.