Applying Data Analytics and Time Series Forecasting for Thorough Ethereum Price Prediction

Finance has been combined with technology to introduce newer advances and facilities in the domain. One such technological advance is cryptocurrency which works on the Blockchain technology. This has proved to be a new topic of research for computer science. However, these currencies are volatile in nature and their forecasting can be really challenging as there are dozens of cryptocurrencies in use all around the world. This chapter uses the time series-based forecasting model for the prediction of the future price of Ethereum since it handles both logistic growth and piece-wise linearity of data. This model is independent as it does not depend on past or historical data which contain seasonality. This model is suitable for real use cases after seasonal fitting using Naïve model, time series analysis, and Facebook Prophet Module (FBProphet). FBProphet Model achieves better accuracy as compared to other models. This chapter aims at drawing a better statistical model with Exploratory Data Analysis (EDA) on the basis of several trends from year 2016 to 2020. Analysis carried out in the chapter can help in understanding various trends related to Ethereum price prediction.