C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

Bai, Chongyang; White, Thomas J.; Xiao, Linda; Subrahmanian, V. S.; Zhou, Ziheng

doi:10.1109/blockchain.2019.00065

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…They also calculated the percentage return by using this model in real crypto trade. [36]Chongyang(2019) collected the trading, socioeconomic and sentiment-based data for training of ML models of 21 cryptocurrencies. They proposed a global algorithm for trend prediction and compared their results with their two competitors.…”

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

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

confidence: 99%

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

Yamin

Chaudhry

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Biswas et al use an LSTM-based prediction model that uses exogenous factors, including stock market capitalization, volume, distribution, and high-end delivery, to predict BTC prices [35]. Bai et al suggest using other cryptocurrencies' price data to classify BTC price movement [36]. In addition, Alessandretti et al introduce a machine learning-based prediction framework based on the daily price data of multiple cryptocurrencies [37].…”

Section: Cryptocurrency Price Predictionmentioning

confidence: 99%

A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

et al. 2022

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Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency's volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework's effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.

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Predictive Analysis of the Cryptocurrencies’ Movement Direction Using Machine Learning Methods

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et al. 2021

Trends in Data Engineering Methods for Intelligent Systems

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C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

Cited by 9 publications

References 14 publications

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

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

A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data

Predictive Analysis of the Cryptocurrencies’ Movement Direction Using Machine Learning Methods

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