Now showing items 1-3 of 3

  • Bitcoin Forecasting Using ARIMA and PROPHET 

    Authors:Yenidogan, Isil; Çayir, Aykut; Kozan, Ozan; Dag, Tugce; Arslan, Cigdem
    Publisher and Date:(IEEE, 2018)
    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 ...

  • Comparison of feature selection algorithms for medical data 

    Data mining application areas widen day by day. Among those areas medical area has been receiving quite a big attention. However working with very large data sets with many attributes is hard. Experts in this field use heavily advanced statistical analysis. The use of data mining techniques is fairly new. This paper compares three feature selection algorithms on medical data sets and comments on the importance of discretization of attributes. © 2012 IEEE.

  • Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods 

    Authors:Çayir, Aykut; Yenidogan, Isil; Daǧ, Hasan
    Publisher and Date:(IEEE, 2018)
    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 ...