Prediction of Top Ten Cryptocurrencies Price through Logistic Regression Based Time Series Analysis Model
Background: The ubiquity of Time Series  analysis is not a new phenomenon. The series of observations which is recorded over an interval of time. Cryptocurrency  is a virtual digital currency in which transactions are verified and records are maintained by a decentralized system using cryptography. Cryptocurrencies are BitCoin(BTC), Ethereum (ETH), Chainlink (Link), Bitcoin Cash (ETC) , XRP (XRP), LiteCoin (LTC), Cardano(ADA), Polkadot (DOT), Binance Coin (BNB), DogeCoin. Among these Bitcoin  is the pioneer because it is the first digital cryptocurrency that shows a significant increase in market capitalization since last few years and more than 70% of the total market capitalization of all cryptocurrencies put together itself. It uses peer to peer technology, and it relies on Blockchain  Technology. Each block stores the information of sender, receiver of transactions and all the blocks are linked together by linked list.
Objective: In proposed work I predict the top ten cryptocurrency prices using Time Series ARIMA  model but due to difficulty of exact nature of ARIMA model it is very difficult to predict appropriate forecasts. Then for greater efficiency I continue my work using Logistic Regression which measures the relationship between the dependent variable with one or more independent variable. Maximum Likelihood Estimation is used to formulate the probabilities.
Methodology: All the real time data were collected from Coinmarketcap.com and historical data from Yahoo! Finance etc. The training data consists of 5 years database and the testing data consists of 6 months from 1st March 2021 to 31st September, 2021. For conducting tests of these approach training data is 70% and testing data is 30%. The Time series generally focuses on real value prediction method which are Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
Result and Discussion: To validate the accuracy of this experimental model it is observed that RMSE  value must be between 0.2 and 0.5 and adjusted R2 value is more than 0.75.
Future Work: I predict the cryptocurrency prices in real time using Logistic Regression model. It can further be improved by two Machine Learning approach Deep Learning and SVM.
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