Time Series Forecasting and ARIMA for Financial Analysis Online Course
Time Series Forecasting and ARIMA for Financial Analysis Online Course
This online course provides a comprehensive introduction to time series analysis and ARIMA for financial forecasting. You’ll begin by understanding the fundamentals of time series data and essential transformation techniques such as power, log, and Box-Cox transformations. The course covers key financial concepts including random walks and baseline forecasts before diving into ARIMA models. You’ll explore autoregressive (AR), moving average (MA), and combined ARIMA models, with practical coding exercises to test stationarity, ACF, PACF, and Auto ARIMA on real financial data. The course also covers forecasting applications for stock returns, sales data, and more, helping you effectively forecast out-of-sample data. Additional sections assist with setting up your coding environment and offer extra support for Python beginners.
Key Benefits
- Thorough introduction to time series analysis, including essential data transformation techniques
- In-depth exploration of financial time series concepts and the importance of baseline forecasting
- Comprehensive study of ARIMA models, supported by hands-on coding exercises for practical application
Target Audience
This course is for financial professionals, data analysts, and individuals with a keen interest in financial analysis, who have a foundational understanding of statistics and Python. While prior experience with financial data is advantageous, it is not a prerequisite.
Learning Objectives
- Gain the ability to understand and analyze time series data effectively
- Implement various data transformation techniques to enhance modeling accuracy
- Apply ARIMA models to analyze and forecast financial data
- Conduct stationarity tests and utilize ACF/PACF for improved model performance
- Use ARIMA techniques for reliable financial data forecasting
- Develop strong data-driven decision-making capabilities for financial analysis
Course Outline
The Time Series Forecasting and ARIMA for Financial Analysis Exam covers the following topics -
Module 1. Course Overview and Structure
Module 2. Getting Started
- Code Access Instructions
Module 3. Fundamentals of Time Series
- Defining a Time Series
- The Difference Between Modeling and Prediction
- Exploring Power, Log, and Box-Cox Transformations
- Feedback and Suggestions (03:10)
Module 4. Financial Foundations
- Introduction to Financial Time Series
- Understanding Random Walks and the Random Walk Hypothesis
- The Naive Forecast and Its Role in Baseline Evaluation
Module 5. ARIMA Model
- Introduction to ARIMA
- Understand Autoregressive Models (AR(p))
- Learn Moving Average Models (MA(q))
- Understanding ARIMA
- Implementing ARIMA in Code
- The Concept of Stationarity
- Stationarity in Code
- ACF (Autocorrelation Function)
- PACF (Partial Autocorrelation Function)
- ACF and PACF Implementation (Part 1)
- ACF and PACF Implementation (Part 2)
- Exploring Auto ARIMA and SARIMAX
- Model Selection Using AIC and BIC
- Auto ARIMA Implementation in Code
- Applying Auto ARIMA to Stock Data
- ACF and PACF for Stock Return Analysis
- Auto ARIMA for Sales Data
- Forecasting with ARIMA
- Out-of-Sample Forecasting Techniques
Module 6. Setting Up Your Development Environment (Appendix)
- Pre-Installation Checklist
- Anaconda Setup for the Environment
- Installation of Essential Libraries (Numpy, Scipy, Matplotlib, Pandas, TensorFlow)
Module 7. Python Coding Assistance for Beginners (Appendix)
- Getting Started with Coding (Part 1)
- Getting Started with Coding (Part 2)
- Demonstrating the Equivalence of Jupyter Notebook vs Traditional Coding
- Using GitHub & Bonus Coding Tips (Optional)
Module 8. Effective Machine Learning Learning Strategies (Appendix)
- Long-Term Success in This Course
- Course Structure: For Beginners or Experts? Practical vs Academic? Fast or Slow Pace?
- Recommended Course Sequence (Part 1)
