A Prediction Model for Bitcoin Cryptocurrency Prices

Arowolo, Micheal Olaolu; Ayegba, Peace; Yusuff, Shakirat Ronke; Misra, Sanjay

doi:10.1007/978-3-030-89546-4_7

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…They employed models such as Logistic Regression, LightGBM, and PCA for enhanced analysis. The work by Arowolo et al ( 2022 ) used ICA-Firefly Linear-SVM and ICA-Firefly-SigmoidSVM to create a high-accuracy prediction model for Bitcoin prices, demonstrating the possibility for real-time use.…”

Section: Related Workmentioning

confidence: 99%

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

Alsini,

Abu Al-Haija,

Alsulami

et al. 2024

Front. Big Data

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IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations.MethodsIn this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly.Results and discussionTo make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

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Section: Related Workmentioning

confidence: 99%

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

Alsini,

Abu Al-Haija,

Alsulami

et al. 2024

Front. Big Data

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“…Few researchers deduced that historical data on cryptocurrency is of use in its market prediction as it follows a random walk. [43]Micheal(2022) used independent component analysis and firefly algorithm along ML models.…”

Section: Survey Of Crypto-currency Market Prediction Using ML Classif...mentioning

confidence: 99%

“…Research [43] claims that cryptocurrency price is a random walk process. Research in this direction requires more effort and exploration.…”

Section: Future Directionsmentioning

confidence: 99%

Cryptocurrency market trend and direction prediction using Machine Learning: A Comprehensive Survey

Yamin

Chaudhry

2023

Preprint

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Bitcoin was the first cryptocurrency introduced as a cryptographic proof-based electronic payment system in 2009. Till now approximately more than 10,000 digital coins are active in the crypto market. Cryptocurrency is a virtual digital asset that uses cryptography and blockchain technology for transaction verification and records maintenance. Its trading is gaining attention due to volatile behavior, decentralized nature, and liquidity in this digital asset. Trading this digital asset provides anonymity and security in transactions. Groundless fluctuations in its price contribute to making its trade risky. Market Prediction of the cryptocurrency is trending because it can reduce the trade loss risk. Data related to this market is vast and publicly available on the internet. It is nearly impossible to infer the market by simple data analysis. Statistical price prediction approaches are less effective due to the absence of seasonality in cryptocurrency market data. Therefore researchers proposed efficient price prediction techniques utilizing statistical, algorithmic, and neural network-based Machine Learning models. This paper provides a detailed literature survey related to the state-of-the-art Machine learning-based prediction methodologies for the market prediction of the digital asset from 2014 to 2022. This research will categorize, summarize, and review the existing research in cryptocurrency market prediction using Machine Learning classifiers. This paper will benefit researchers to be productive in the right direction in the future.

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“…Thus, the integration of the Internet of Things (IoT) and other tools have been used to resolve urban issues and problems [6,7]. IoT-enabled applications are critical for making the best use of existing resources and technologies in the development of SCs.…”

Section: Introductionmentioning

confidence: 99%

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

Awotunde,

Gaber,

Prasad

et al. 2023

SCPE

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The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.

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A Prediction Model for Bitcoin Cryptocurrency Prices

Cited by 5 publications

References 43 publications

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions

Cryptocurrency market trend and direction prediction using Machine Learning: A Comprehensive Survey

Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

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