Time Series Forecasting and ARIMA for Financial Analysis Online Course

Time Series Forecasting and ARIMA for Financial Analysis Online Course

This course introduces financial analysis with ChatGPT using pairs trading, blending AI capabilities with financial expertise to generate smarter investment decisions, risk assessments, and planning strategies. You’ll start with an overview of ChatGPT, project scope, and course tools before diving into pairs trading—covering trading intuition, signal correction, z-score computation, returns analysis, and strategy testing. Through building a trading bot, you’ll explore concepts like log returns, cumulative returns, and long-only strategies. By the end, you’ll know how to leverage ChatGPT effectively for financial analysis, enhancing productivity, decision-making, and trading performance with pairs trading.

Who should take the course?

This course is ideal for financial analysts, data scientists, traders, and business professionals who want to apply time series forecasting techniques to financial data. It’s well-suited for those looking to understand and implement ARIMA models to analyze trends, forecast market behavior, and make data-driven decisions. Whether you’re working in finance, economics, or business strategy, this course will help you gain practical skills in forecasting and predictive analysis.

What you will learn

• Understand and analyze time series data
• Implement data transformations for improved modeling
• Apply ARIMA models to financial data
• Perform stationarity tests and utilize ACF/PACF
• Forecast financial data using ARIMA techniques
• Develop data-driven decision-making skills

Course Outline

Welcome

• Introduction and Outline
• Special Offer

Getting Set Up

• Warmup (Optional)
• Where to get the code

Time Series Basics

• What is a Time Series?
• Modeling vs. Predicting
• Power, Log, and Box-Cox Transformations
• Suggestion Box (03:10)

Financial Basics

• Financial Time Series Primer
• Random Walks and the Random Walk Hypothesis
• The Naive Forecast and the Importance of Baselines

ARIMA

• ARIMA Section Introduction
• Autoregressive Models - AR(p)
• Moving Average Models - MA(q)
• ARIMA
• ARIMA in Code
• Stationarity
• Stationarity in Code
• ACF (Autocorrelation Function)
• PACF (Partial Autocorrelation Function)
• ACF and PACF in Code (pt 1)
• ACF and PACF in Code (pt 2)
• Auto ARIMA and SARIMAX
• Model Selection, AIC and BIC
• Auto ARIMA in Code
• Auto ARIMA in Code (Stocks)
• ACF and PACF for Stock Returns
• Auto ARIMA in Code (Sales Data)
• How to Forecast with ARIMA
• Forecasting Out-Of-Sample
• ARIMA Section Summary

Setting Up Your Environment (Appendix)

• Pre-Installation Check
• Anaconda Environment Setup
• How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow

Extra Help With Python Coding for Beginners (Appendix)

• How to Code Yourself (part 1)
• How to Code Yourself (part 2)
• Proof that using Jupyter Notebook is the same as not using it
• How to use Github & Extra Coding Tips (Optional)

Effective Learning Strategies for Machine Learning (Appendix)

• How to Succeed in this Course (Long Version)
• Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
• What order should I take your courses in? (part 1)
• What order should I take your courses in? (part 2)