Financial Analysis with ARIMA and Time Series Forecasting: Master Time Series Forecasting with ARIMA for Financial Analysis

Begin with an introduction to time series analysis, providing a solid foundation for understanding the nature and structure of time series data. You'll explore key concepts such as modeling versus predicting, and learn essential data transformation techniques including power, log, and Box-Cox transformations. These fundamentals set the stage for more advanced topics. As you delve deeper, you'll encounter a thorough examination of financial time series. You'll learn about random walks, the random walk hypothesis, and the importance of baseline forecasts. The course then transitions to a comprehensive study of ARIMA models. You'll explore autoregressive models (AR), moving average models (MA), and the combination of these in ARIMA. Practical coding sessions will reinforce your understanding, allowing you to apply stationarity tests, ACF, PACF, and Auto ARIMA techniques to real financial data. The latter part of the course focuses on the application of ARIMA models in forecasting. You'll learn how to implement ARIMA in various scenarios, from stock returns to sales data. The course wraps up with a detailed guide on forecasting out-of-sample data, ensuring you can apply your new skills in real-world situations. Supplementary sections offer guidance on setting up your coding environment and additional help for Python beginners.

This course is designed for financial professionals, data analysts, and enthusiasts with a basic understanding of statistics and Python. Prior experience with financial data is beneficial but not required.