Model Comparison for Price Prediction in Blockchain-Enabled Internet of Things (IoT) Assets

Çağatay Ersin; Onur Köktürk

Article Details

Model Comparison for Price Prediction in Blockchain-Enabled Internet of Things (IoT) Assets

Authors:

Çağatay Ersin | Onur Köktürk

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

İstatistiksel Veri Bilimi

Journal:

Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1057-1066

Document Type:

Article

Publication Date:

7/2025

Document Language:

Turkish

Document ID (Org No.):

2001346261

pISSN:

2619-8991

Keywords:

Abstract:

 With the acceleration of technological developments, the Internet of Things IoT contributes to a wide range of transformations, from global production processes to smart city applications, from personal health monitoring devices to smart sportswear. The Internet of Things creates an ecosystem where devices interact with each other and data centers to produce large amounts of data. In this context, blockchain-based crypto assets based on IoT technologies have also developed. The aim of this study is to make a price prediction for IoT themed crypto assets Vechain VET, IOTA and Helium HNT. In the study, the daily closing prices of Vechain and IOTA between 02.01.2020 - 10.12.2024, and Helium coin between 09.06.2020 - 10.12.2024 were used. Decision Trees, Random Forest and Support Vector Regression SVR models were compared for price prediction. The decision tree model aims to predict future price movements based on past price data. The Random Forest algorithm provides more robust and generalizable predictions by using the ensemble learning method formed by combining multiple decision trees. Support Vector Regression SVR creates non-linear regression models using hyperplanes to better capture complex relationships between data points. The performance of the models was evaluated with metrics such as Mean Squared Error MSE and R² R-squared score. The results showed that the Random Forest model provided high accuracy in predictions for Helium and IOTA coins, while SVR modelled better for VeChain coin. The Decision Tree model was successful in producing independent predictions but showed relatively lower performance than other models.

 With the acceleration of technological developments, the Internet of Things IoT contributes to a wide range of transformations, from global production processes to smart city applications, from personal health monitoring devices to smart sportswear. The Internet of Things creates an ecosystem where devices interact with each other and data centers to produce large amounts of data. In this context, blockchain-based crypto assets based on IoT technologies have also developed. The aim of this study is to make a price prediction for IoT themed crypto assets Vechain VET, IOTA and Helium HNT. In the study, the daily closing prices of Vechain and IOTA between 02.01.2020 - 10.12.2024, and Helium coin between 09.06.2020 - 10.12.2024 were used. Decision Trees, Random Forest and Support Vector Regression SVR models were compared for price prediction. The decision tree model aims to predict future price movements based on past price data. The Random Forest algorithm provides more robust and generalizable predictions by using the ensemble learning method formed by combining multiple decision trees. Support Vector Regression SVR creates non-linear regression models using hyperplanes to better capture complex relationships between data points. The performance of the models was evaluated with metrics such as Mean Squared Error MSE and R² R-squared score. The results showed that the Random Forest model provided high accuracy in predictions for Helium and IOTA coins, while SVR modelled better for VeChain coin. The Decision Tree model was successful in producing independent predictions but showed relatively lower performance than other models.

 With the acceleration of technological developments, the Internet of Things IoT contributes to a wide range of transformations, from global production processes to smart city applications, from personal health monitoring devices to smart sportswear. The Internet of Things creates an ecosystem where devices interact with each other and data centers to produce large amounts of data. In this context, blockchain-based crypto assets based on IoT technologies have also developed. The aim of this study is to make a price prediction for IoT themed crypto assets Vechain VET, IOTA and Helium HNT. In the study, the daily closing prices of Vechain and IOTA between 02.01.2020 - 10.12.2024, and Helium coin between 09.06.2020 - 10.12.2024 were used. Decision Trees, Random Forest and Support Vector Regression SVR models were compared for price prediction. The decision tree model aims to predict future price movements based on past price data. The Random Forest algorithm provides more robust and generalizable predictions by using the ensemble learning method formed by combining multiple decision trees. Support Vector Regression SVR creates non-linear regression models using hyperplanes to better capture complex relationships between data points. The performance of the models was evaluated with metrics such as Mean Squared Error MSE and R² R-squared score. The results showed that the Random Forest model provided high accuracy in predictions for Helium and IOTA coins, while SVR modelled better for VeChain coin. The Decision Tree model was successful in producing independent predictions but showed relatively lower performance than other models.

 Teknolojik gelişmelerin hız kazanmasıyla birlikte Nesnelerin İnterneti IoT - Internet of Things, küresel ölçekte üretim süreçlerinden akıllı şehir uygulamalarına, kişisel sağlık izleme cihazlarından akıllı spor kıyafetlerine kadar geniş bir yelpazede dönüşüme katkı sağlamaktadır. Nesnelerin İnterneti, cihazların birbirleriyle ve veri merkezleriyle etkileşime geçerek büyük miktarda veri ürettiği bir ekosistem yaratmaktadır. Bu bağlamda, IoT teknolojilerine dayalı blok zinciri tabanlı kripto varlıklar da gelişim göstermiştir. Bu çalışmanın amacı, IoT temalı kripto varlıklardan Vechain VET, IOTA ve Helium HNT için fiyat tahmini yapmaktır. Çalışmada 02.01.2020 - 10.12.2024 tarihleri arasındaki Vechain ve IOTA, 09.06.2020 - 10.12.2024 tarihleri arasındaki Helium coinin günlük kapanış fiyatları kullanılmıştır. Fiyat tahmini için Karar Ağaçları Decision Trees, Rassal Orman Random Forest ve Destek Vektör Regresyonu SVR modelleri karşılaştırılmıştır. Karar ağaçları modeli, geçmiş fiyat verilerini baz alarak gelecekteki fiyat hareketlerini tahmin etmeyi amaçlamaktadır. Rassal Orman algoritması, birden fazla karar ağacının bir araya getirilmesiyle oluşan topluluk öğrenme yöntemini kullanarak daha sağlam ve genellenebilir tahminler sunmaktadır. Destek Vektör Regresyonu SVR ise veri noktaları arasındaki karmaşık ilişkileri daha iyi yakalamak için hiper düzlemleri kullanarak doğrusal olmayan regresyon modelleri oluşturur. Modellerin performansı Mean Squared Error MSE ve R² R-kare skoru gibi metriklerle değerlendirilmiştir. Sonuçlar, Rassal Orman modelinin Helium ve IOTA coinlerin tahminlerinde yüksek doğruluk sağladığını, VeChain coin için ise SVRnin daha iyi modellediğini ortaya koymuştur. Karar Ağaçları modeli, bağımsız tahminler üretme konusunda başarılı olmakla birlikte diğer modellere göre nispeten daha düşük performans göstermiştir.

Article Details

Model Comparison for Price Prediction in Blockchain-Enabled Internet of Things (IoT) Assets

Authors:

Çağatay Ersin | Onur Köktürk

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

İstatistiksel Veri Bilimi

Journal:

Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1057-1066

Document Type:

Article

Publication Date:

7/2025

Document Language:

Turkish

Document ID (Org No.):

2001346261

pISSN:

2619-8991

Cryptocurrency

Decision trees

Random forest

Support vector regressionKripto varlıklar

Karar ağaçları

Rassal orman

Destek vektör regresyonu

Issues

Article Details

Authors: Çağatay Ersin | Onur Köktürk

Publisher: Karyay Karadeniz Yayımcılık

Main Topics: İstatistiksel Veri Bilimi

Journal: Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1057-1066

Document Type: Article

Publication Date: 7/2025

Document Language: Turkish

Document ID (Org No.): 2001346261

pISSN: 2619-8991

Issues

Authors:

Çağatay Ersin | Onur Köktürk

Publisher:

Karyay Karadeniz Yayımcılık

Main Topics:

İstatistiksel Veri Bilimi

Journal:

Black Sea Journal of Engineering and Science Vol.8 Issue 4, pp.1057-1066

Document Type:

Article

Publication Date:

7/2025

Document Language:

Turkish

Document ID (Org No.):

2001346261

pISSN:

2619-8991