CryptoForecasting using Machine and Deep learning (Part 1)

CryptoForecasting using Machine Learning

The main aspect of predicting the stock-related data is its variance with time. We can project the possible price of the dataset when it reaches a specific time.

Part – I Forecasting prices using Facebook/Meta’s Prophet model

Developed by Facebook’s Core Data Science Team, FBProphet is widely used in machine learning for forecasting time series for instances that involve time series data with all
kinds of seasonalities (yearly, weekly and monthly) including holidays and vacations. This is part one of the series on CryptoForecasting using Machine Learning.
I have used Facebook’s Prophet model to predict the model for the same.

Prophet is also suitable for historical data with several seasons. To carry out the process of regression, FBProphet uses time as a regression variable (regressor) along with the
time series’ linear and non-linear parameters as components. The data can be fitted into the model which can be changed from linear (default) to non-linear in FBProphet as per the
requirements.

GitHub

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