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At present, cryptocurrencies have become a global phenomenon in financialsectors as it is one of the most traded financial instruments worldwide.Cryptocurrency is not only one of the most complicated and abstruse fieldsamong financial instruments, but it is also deemed as a perplexing problem infinance due to its high volatility. This paper makes an attempt to applymachine learning techniques on the index and constituents of cryptocurrencywith a goal to predict and forecast prices thereof. In particular, the purposeof this paper is to predict and forecast the close (closing) price of thecryptocurrency index 30 and nine constituents of cryptocurrencies using machinelearning algorithms and models so that, it becomes easier for people to tradethese currencies. We have used several machine learning techniques andalgorithms and compared the models with each other to get the best output. Webelieve that our work will help reduce the challenges and difficulties faced bypeople, who invest in cryptocurrencies. Moreover, the obtained results can playa major role in cryptocurrency portfolio management and in observing thefluctuations in the prices of constituents of cryptocurrency market. We havealso compared our approach with similar state of the art works from theliterature, where machine learning approaches are considered for predicting andforecasting the prices of these currencies. In the sequel, we have found thatour best approach presents better and competitive results than the best worksfrom the literature thereby advancing the state of the art. Using suchprediction and forecasting methods, people can easily understand the trend andit would be even easier for them to trade in a difficult and challengingfinancial instrument like cryptocurrency.

Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning