Forecast Crypto Market with Time Series & Machine Learning

Welcome to Forecasting Cryptocurrency Market with Prophet, Time Series & Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to perform complex analysis and visualization on cryptocurrency market dataset. This course will be focusing mainly on forecasting cryptocurrency prices using three different forecasting models, those are Prophet, time series decomposition, and machine learning particularly we are going to be utilizing Random Forest and XGBoost. Regarding programming language, we are going to use Python alongside with several libraries like Pandas for performing data modeling, Numpy for performing complex calculations, Matplotlib for visualizing the data, and TensorFlow which is an open-source machine learning library used for building and training various deep learning models. Meanwhile, for the data source, we are going to download the crypto market dataset from Kaggle. In the introduction session, you will learn basic fundamentals of cryptocurrency market forecasting, such as getting to know the crypto market characteristics and forecasting models that will be used. Then, continue by learning the basic mathematics behind prophet model and time series decomposition where you will be guided step by step on how to analyze case study and perform basic calculation. This session is intended to prepare your knowledge and understanding before implementing these models in the forecasting project. Afterward, you will also learn several factors which can potentially impact the cryptocurrency market, such as liquidity, market cap, transaction volume, and circulating supply. Once you’ve learnt all necessary knowledge about crypto market forecasting, we will begin the project, firstly you will be guided step by step on how to set up Google Colab since we are going to use it as the IDE in this project, then you will also learn how to find and download datasets from Kaggle. After preparing the IDE and datasets, you will enter the main section of the course which is the project section. The project will be consisted of three parts, the first one is forecasting cryptocurrency market using Prophet model, the second one is forecasting cryptocurrency market using time series decomposition model, meanwhile, the third one is forecasting cryptocurrency market using machine learning models specifically Random Forest and XGBoost. Lastly, at the end of the course, you will also learn how to perform model evaluations to assess the accuracy and quality of your forecasting model.

First of all, before getting into the course, we need to ask ourselves these questions: why should we learn to forecast the crypto market? Is it going to be accurate? Well, there are many answers to those questions. Firstly, both cryptocurrency and big data technology have advanced very rapidly in the past few years, therefore, combining both sounds like a brilliant idea. In addition to that, integrating big data technology especially machine learning and time series will enable us to make more accurate data driven based predictions. Not only that, identifying patterns and trends from the historical data can be used as a good indicator to forecast what will happen in the future. Nonetheless, no matter how advanced or accurate your forecasting model is, you still need to be aware that there is no such thing as 100% accuracy when it comes to forecasting. Last but not least, learning how to forecast can be very valuable knowledge and skill sets since you will be able to implement the same exact concept to other markets like stock market, commodity market, or even real estate market.

Below are things that you can expect to learn from the course:

• Learn basic fundamentals of cryptocurrency market forecasting, such as getting to know crypto market characteristics and forecasting models that will be used

• Learn math and logics behind prophet forecasting model, such as getting to know trend factor, seasonality component, and holiday component

• Learn math and logics behind time series decomposition model, such as getting to know trend component, seasonal component, and residual component

• Learn how to split dataset using Random Forest algorithm and learn how to calculate Gini Impurity

• Learn several factors that can potentially impact cryptocurrency market, such as circulating supply, transaction volume, liquidity, market cap, and security

• Learn how to find and download datasets from Kaggle

• Learn how to upload data to Google Colab Studio

• Learn how to clean datasets from missing values and duplicate values

• Learn how to detect outliers in the dataset

• Learn how to analyse and visualise daily and annual price volatility

• Learn how to detect market trend and calculate moving average

• Learn how to find correlation between price and volume using TensorFlow

• Learn how to build forecasting model using Prophet

• Learn how to build forecasting model using time series decomposition

• Learn how to build forecasting model using machine learning, specifically Random Forest and XGBoost algorithm

• Learn how to evaluate the accuracy and quality of the forecasting models using prediction interval coverage, component analysis, and feature importance analysis

Additional Projects

Analyzing Market Sentiment Using Spacy
In this project, you'll use Spacy, a powerful natural language processing (NLP) library, to analyze market sentiment by processing and understanding text data. By analyzing financial news, social media, and other sources of market-related text, you can determine whether the sentiment is positive, negative, or neutral, helping you make informed decisions based on market psychology.

Forecasting Price Using Support Vector Regression (SVR)
In this project, you'll apply Support Vector Regression (SVR), a machine learning algorithm, to forecast prices based on historical data. SVR is particularly useful for predicting continuous values, like stock prices or market trends, by finding the optimal hyperplane that best fits the data and minimizes prediction errors, helping you make data-driven price forecasts.