Prediction of Bitcoin prices with machine learning methods using time series data [Zaman serisi verilerini kullanarak makine ögrenmesi yöntemleri ile bitcoin fiyat tahmini]

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model. © 2018 IEEE.

Yazar Karasu S.
Altan A.
Sarac Z.
Hacioglu R.
Yayın Türü Conference Object
Tek Biçim Adres https://hdl.handle.net/20.500.12628/7104
Tek Biçim Adres 10.1109/SIU.2018.8404760
Konu Başlıkları Bitcoin
Cryptocurrency
Machine Learning
Price Prediction
Support Vector Machine (SVM)
Koleksiyonlar Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD
Scopus İndeksli Yayınlar Koleksiyonu
Dergi Adı 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Sayfalar 1 - 4
Yayın Yılı 2018
Eser Adı
[dc.title]
Prediction of Bitcoin prices with machine learning methods using time series data [Zaman serisi verilerini kullanarak makine ögrenmesi yöntemleri ile bitcoin fiyat tahmini]
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
conferenceObject
Açıklama
[dc.description]
Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas
Açıklama
[dc.description]
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780
Özet
[dc.description.abstract]
In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training phase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model. © 2018 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Yılı
[dc.date.issued]
2018
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/SIU.2018.8404760
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/7104
Yayın Dili
[dc.language.iso]
tur
Konu Başlıkları
[dc.subject]
Bitcoin
Konu Başlıkları
[dc.subject]
Cryptocurrency
Konu Başlıkları
[dc.subject]
Machine Learning
Konu Başlıkları
[dc.subject]
Price Prediction
Konu Başlıkları
[dc.subject]
Support Vector Machine (SVM)
Yazar
[dc.contributor.author]
Karasu S.
Yazar
[dc.contributor.author]
Altan A.
Yazar
[dc.contributor.author]
Sarac Z.
Yazar
[dc.contributor.author]
Hacioglu R.
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Yazar Departmanı
[dc.contributor.department]
Zonguldak Bülent Ecevit Üniversitesi
İlk Sayfa Sayısı
[dc.identifier.startpage]
1
Son Sayfa Sayısı
[dc.identifier.endpage]
4
Dergi Adı
[dc.relation.journal]
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
ISBN
[dc.identifier.isbn]
9781538615010
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
220
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
16 Temmuz 2024 21:45
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Tıklayınız
different Bitcoin prediction performance Squared obtained weight coefficients window lengths independent construct training indicators measured higher proposed Correlation Pearson (RMSE) Absolute cross-validation statistical method Filters 10-fold consisting series methods learning machine Machine closing Vector Support
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