APPLICATION OF MACHINE LEARNING ALGORITHMS TO PREDICT CRYPTOCURRENCY PRICES / (Kayıt no. 292753)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 02753nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20240923111705.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240912d2024 cy d|||| |||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 3460 |
| Cutter no | D67 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Doroodiaİn, Shayan, |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | APPLICATION OF MACHINE LEARNING ALGORITHMS TO PREDICT CRYPTOCURRENCY PRICES / |
| Sorumluluk Bildirimi | SHAYAN DOROODIAIN ; SUPERVISOR, ASST. PROF. DR. EMRE ÖZBİLGE |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 62 sheets; |
| Birlikteki Materyal | + 1 CD ROM |
| Boyutları | 30 cm |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineerinig |
| 520 ## - ÖZET NOTU | |
| Özet notu | Digital money has become a popular investment choice due to its decentralized structure and the potential for large returns. However, the cryptocurrency market’s extremely unpredictable and volatile character makes it difficult for investors to foresee price fluctuations and make lucrative purchases. One of the most popular and effective price forecasting approaches is time series analysis, which detects trends and patterns in previous pricing data to estimate future price movements. The combination of machine learning (ML) methods and time series analysis can greatly improve forecasting accuracy.<br/>Predicting future Bitcoin prices is crucial for customers to maximize their profits and minimize their losses. However, this task is challenging because of the complex temporal relationships between Bitcoin-related features. Moreover, external factors can influence cryptocurrency movement, resulting in unpredictable price fluctuations. To address this problem, deep recurrent neural network (DRNN)-based sequence learner models have been used to learn complex sequential features. In this study, multiple bidirectional versions of LSTM, GRU, and RNN recurrent layers were designed on DRNN models, and their performances were compared for a one-day-ahead Bitcoin price prediction task. Algorithms are trained and evaluated using data generated from Yahoo Finance API historical price data from May 2017 to May 2023. After this model is tested using various criteria, At the end of the day we will have a complete comparison table with all data to show the best model in each scenario.<br/>The results show that using a convolutional layer with three bidirectional GRU layer-based DRNN models achieves superior performance, with an average deviation of 3.81% from the actual Bitcoin price. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Computer Engineerinig |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Özbilge, Emre |
| İlişkili Terim | supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 12.09.2024 | Bağış | YL 3460 D67 2024 | T3877 | 12.09.2024 | C.1 | 12.09.2024 | Thesis | Computer Engineerinig | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 12.09.2024 | Bağış | YL 3460 D67 2024 | CDT3877 | 12.09.2024 | C.1 | 12.09.2024 | Suppl. CD | Computer Engineerinig |