Last Updated on September 4, 2023 by GeeksGod
Course : Forecast Crypto Market with Time Series & Machine Learning
Forecasting Cryptocurrency Market with Prophet, Time Series & Machine Learning
Welcome to the Forecasting Cryptocurrency Market with Prophet, Time Series & Machine Learning course. This comprehensive project-based course will guide you step by step on how to perform complex analysis and visualization on cryptocurrency market datasets. The main focus of this course is on forecasting cryptocurrency prices using three different models: Prophet, time series decomposition, and machine learning with Random Forest and XGBoost.
Why Forecast the Crypto Market?
Before diving into the course content, let’s address the question of why we should learn to forecast the crypto market and whether it will be accurate. There are several reasons why forecasting the cryptocurrency market is essential:
- Advancement in Cryptocurrency and Big Data Technology: Both cryptocurrency and big data technology have rapidly advanced in recent years. By combining the two, we can leverage the power of big data and machine learning to make more accurate predictions.
- Data-driven Predictions: Analyzing historical data helps identify patterns and trends that can serve as valuable indicators for future predictions.
- Transferable Knowledge and Skills: Learning how to forecast the cryptocurrency market equips you with valuable knowledge and skill sets that can be applied to other markets, such as stock market, commodity market, and real estate market.
However, it is important to note that even with advanced forecasting models, 100% accuracy is not guaranteed. Forecasting models provide insights and probabilities, but they cannot predict the future with absolute certainty.
What Will You Learn?
This course covers a wide range of topics related to cryptocurrency market forecasting. By the end of the course, you can expect to learn:
- The basic fundamentals of cryptocurrency market forecasting, including an understanding of crypto market characteristics and the forecasting models used.
- The mathematics and logic behind the Prophet forecasting model, including trend factor, seasonality component, and holiday component.
- The mathematics and logic behind the time series decomposition model, including trend component, seasonal component, and residual component.
- How to split datasets using the Random Forest algorithm and calculate Gini Impurity.
- Factors that can potentially impact the cryptocurrency market, such as circulating supply, transaction volume, liquidity, market cap, and security.
- How to find and download datasets from Kaggle.
- How to upload data to Google Colab Studio.
- How to clean datasets by handling missing values and duplicate values.
- How to detect outliers in the dataset.
- How to analyze and visualize daily and annual price volatility.
- How to detect market trends and calculate moving averages.
- How to find correlations between price and volume using TensorFlow.
- How to build forecasting models using the Prophet model.
- How to build forecasting models using time series decomposition.
- How to build forecasting models using machine learning algorithms, specifically Random Forest and XGBoost.
- How to evaluate the accuracy and quality of the forecasting models using prediction interval coverage, component analysis, and feature importance analysis.
The course is divided into several sections to guide you through the learning process:
In the introduction session, you will learn the basic fundamentals of cryptocurrency market forecasting. This includes understanding the characteristics of the crypto market and the forecasting models that will be used throughout the course.
Prophet Model and Time Series Decomposition
In this section, you will dive deeper into the mathematics and logic behind the Prophet model and time series decomposition. You will learn how to analyze case studies and perform basic calculations to prepare yourself for implementing these models in the forecasting project.
Impact Factors on the Cryptocurrency Market
This section explores several factors that can potentially impact the cryptocurrency market, such as liquidity, market cap, transaction volume, and circulating supply. Understanding these factors will enhance your ability to make accurate predictions.
Setting Up Google Colab and Data Source
To kick off the project, you will be guided through the process of setting up Google Colab as the integrated development environment (IDE) for this course. Additionally, you will learn how to find and download the crypto market dataset from Kaggle.
The main section of the course is dedicated to the project itself. It is divided into three parts:
1. Forecasting Cryptocurrency Market using Prophet Model
In this part, you will learn how to build a forecasting model using the Prophet model. Step-by-step guidance will be provided to help you effectively analyze and predict cryptocurrency market trends.
2. Forecasting Cryptocurrency Market using Time Series Decomposition Model
Here, you will explore another approach to forecasting by using the time series decomposition model. You will gain hands-on experience in implementing this model and analyzing the results.
3. Forecasting Cryptocurrency Market using Machine Learning
The final part of the project focuses on using machine learning algorithms, specifically Random Forest and XGBoost, to forecast the cryptocurrency market. This section will equip you with the skills to build and train models using these algorithms.
In the last part of the course, you will learn how to evaluate the accuracy and quality of your forecasting models. Techniques such as prediction interval coverage, component analysis, and feature importance analysis will be covered.
By the end of this course, you will have gained valuable knowledge and skills in forecasting the cryptocurrency market. The insights and techniques learned can be applied not only to the crypto market but also to other financial markets. Start your journey now and enhance your understanding of cryptocurrency market forecasting!
Remember, learning is a continuous process, and practicing what you have learned is key to honing your skills. Harness the power of big data and machine learning to make accurate predictions and gain a competitive edge in the cryptocurrency market!