Time Series Analysis of Cryptocurrencies Using Deep Learning & Fbprophet

Abstract

This paper consists of cryptocurrency prediction and analysis using different algorithms, the major cryptocurrency took into account for analysis and prediction are Bitcoin (BTC), Ethereum (ETH), Chainlink (LINK), Bitcoin Cash (BTC), XRP (XRP). Nowadays, investing in cryptocurrency has become a major deal, with huge cash flow and billions of industries which has taken over the small industry that was over the past. With this investment, it is important to understand the high & low of a particular cryptocurrency and what output will be generated with such decisions. Prediction of cryptocurrencies is tangible and requires lots of understanding regarding the flow of money on daily basis. The machine learning industry has advanced to a great extent and it would further do, this advancement has led us to a bigger problem-solving technique, that is prediction of data or analysis of trend which can be in any format. The format in this paper is a time series analysis of the daily high-low-close of digital currency. The algorithms used for such analysis is LSTM (Long Short-Term Memory) which is part of Deep Learning and further Fbprophet which is an Auto Machine Learning for prediction is used. The metric used for the analysis of the algorithm is MAE (Mean Absolute Error). The programming language used is Python, which solves the majority of use cases.