IoT 18: e3

Research Article

System for Analysis and Prediction of Trends in Cryptocurrency Market

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  • @ARTICLE{10.4108/eai.10-3-2022.173608,
        author={Shaad Iqbal Ansari and Vani H Y},
        title={System for Analysis and Prediction of Trends in Cryptocurrency Market},
        journal={EAI Endorsed Transactions on Internet of Things: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={IOT},
        year={2022},
        month={3},
        keywords={Blockchain, Bitcoin, Time Series, Forecasting, Regression, Machine Learning, Neural Networks, Crypto currency},
        doi={10.4108/eai.10-3-2022.173608}
    }
    
  • Shaad Iqbal Ansari
    Vani H Y
    Year: 2022
    System for Analysis and Prediction of Trends in Cryptocurrency Market
    IOT
    EAI
    DOI: 10.4108/eai.10-3-2022.173608
Shaad Iqbal Ansari1, Vani H Y2,*
  • 1: Department of Information Science and Engg JSSSTU, Mysore, India
  • 2: Assistant Professor Department of Information Science and Engg JSSST University Mysuru, India
*Contact email: vanihy@sjce.ac.in

Abstract

In this article forecasting of daily closing price series of Bitcoin, Ripple, Dash, Litecoin and Ethereum crypto currencies, using data on prices (open, low, high), market capital and volumes using prior days is focused. The value conduct of cryptographic forms of money remains to a great extent neglected, giving new chances to scientists and business analysts to feature the likenesses and contrasts with standard monetary costs. Hence the paper is focused on this area. he results are compared with various benchmarks. Predictions are done using statistical techniques and machine learning algorithms. A simple linear regression (SLR) model that uses only a single-variable sequence of closing prices for forecasting, and a multiple linear regression (MLR) model that uses a multivariate sequence of prices and quantities at the same time. The simple linear regression (SLR) model for univariate serial forecasting uses only closing prices. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (relative RMSE) performance measures are considered. The accuracy achieved by the ARIMA model on our dataset is the highest, followed by Multivariable Linear Regression and LSTM.