Price Prediction of Bitcoin using Social Media Activities and Past Trends

Abstract

The world's most well-known cryptocurrency, Bitcoin, is much attractive to financial market players due to its recent price boom and fall. It is very difficult to anticipate because of the substantial volatility of the Bitcoin conversion scale. Forecasting of its behavior is crucial for the financial industry sectors. Some of the research works have been used machine learning techniques on past trends data of Bitcoin for its price predictions but they lack in accuracy. Therefore, the aim of this paper is to look into the global crypto-currency price movement trends about social media communication data as well as historical data trends. The thought is to analyze informative trends in online groups and social media networks to fully understand and extract useful information which could be used to improve the prediction accuracy of cryptocurrency price fluctuations. In this work we examine the behavior of Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN) with LSTM and GRU, ARIMA model, Random forests(RF), and others for price direction predictions. Similarly, the SVM and ANN are used to predict Bitcoin’s minimum, maximum, and closing prices. The outcomes of the forecasts are also utilized as inputs to improve forecasts of price movement. The results showed that performance was greatly improved by the characteristics that were picked as well as by the effective machine learning and deep learning techniques.