inis 18: e2

Research Article

Prediction of dogecoin price using deep learning and social media trends

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  • @ARTICLE{10.4108/eai.29-9-2021.171188,
        author={Basant Agarwal and Priyanka Harjule and Lakshit Chouhan and Upkar Saraswat and Himanshu Airan and Parth Agarwal},
        title={Prediction of dogecoin price using deep learning and  social media trends},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems: Online First},
        keywords={Dogecoin, Cryptocurrency, Deep Learning, Sentiment Analysis},
  • Basant Agarwal
    Priyanka Harjule
    Lakshit Chouhan
    Upkar Saraswat
    Himanshu Airan
    Parth Agarwal
    Year: 2021
    Prediction of dogecoin price using deep learning and social media trends
    DOI: 10.4108/eai.29-9-2021.171188
Basant Agarwal1, Priyanka Harjule2,*, Lakshit Chouhan1, Upkar Saraswat1, Himanshu Airan1, Parth Agarwal1
  • 1: Department of Computer Science and Engineering, Indian Institute of Information Technology Kota (IIIT Kota)
  • 2: Department of Mathematics, Malaviya National Institute of Technology Jaipur (MNIT Jaipur)
*Contact email:


INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance. In 2021, over 4,000 cryptocurrencies are already listed. There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin. Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency.

OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency. The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency.

METHODS: The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags “Dogecoin” and “Doge”. Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market.

RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset.

RESULTS: Experimental results show that the proposed approach performs efficiently.