ISSN 2394-5125

    RNN-LSTM Model based Forecasting of Cryptocurrency Prices using Standard Scaler Transform (2023)

    Ch. Shivani, B. Anusha, B. Druvitha, K. Kumara Swamy
    JCR. 2023: 144-148


    Bitcoin is the worlds’ most valuable cryptocurrency and is traded on over 40 exchanges worldwide accepting over 30 different currencies. It has a current market capitalization of 9 billion USD according to and sees over 250,000 transactions taking place per day. As a currency, Bitcoin offers a novel opportunity for price prediction due to its relatively young age and resulting volatility, which is far greater than that of fiat currencies. It is also unique in relation to traditional fiat currencies in terms of its open nature; no complete data exists regarding cash transactions or money in circulation for fiat currencies. Prediction of mature financial markets such as the stock market has been researched at length. Bitcoin presents an interesting parallel to this as it is a time series prediction problem in a market still in its transient stage. Traditional time series prediction methods such as Holt-Winters exponential smoothing models rely on linear assumptions and require data that can be broken down into trend, seasonal and noise to be effective. This type of methodology is more suitable for a task such as forecasting sales where seasonal effects are present. Therefore, the analysis of financial data for predicting the future bitcoin price has always been an important field of research with a direct and indirect effect on world economy. Due to the lack of seasonality in the Bitcoin market and its high volatility, these methods are not highly effective for this task. Given the complexity of the task, machine learning makes for an interesting technological solution based on its performance in similar areas. Hence, a time series analysis is utilized in this paper in order to find out the pattern of bitcoin price movement and forecasting the closing price of the next few days as well as analyzing the performance of the time series models.


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    Volume & Issue

    Volume 10 Issue-1