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@article{159274,
        author = {Shubham Bhattad and Stefin Sunnymon and Dallas Vaz and Chhaya Dhavale},
        title = {CRYPTOCURRENCY PRICE FORECASTING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {850-854},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159274},
        abstract = {Cryptocurrency is a class of digital asset that is very challenging to monitor and forecast. Predicting cryptocurrency price action and its locus is difficult because it does not coincide with market movements. Our objective is to analyze the machine learning algorithms used in various researches and find out the best model which can be used to forecast the prices of time series models. In this work, we have compared and analyzed earlier methodologies in which several machine learning models were applied to forecast the trend of cryptocurrency time series data. The outcomes support the machine learning models' ability to predict trends reasonably well. In total, there are approximately 3000 cryptocurrencies. People are frequently baffled about which coin to invest in for a profitable future. This paper analyzes the trends of the various crypto currencies and predicts the closing price of a cryptocurrency.},
        keywords = {Cryptocurrency, Bitcoin, Ethereum, Prophet, XG-Boost, Auto Regressive Integrated Moving Average (ARIMA), Long Short-term Memory model (LSTM), Yfinance, Time Series data, Machine Learning.},
        month = {},
        }