Financial Analysis with ARIMA and Time Series Forecasting

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course will provide you with a deep understanding of how to analyze financial data using ARIMA and time series forecasting. You will learn the foundational techniques required to model and predict financial time series, equipping you with the skills to apply these methods to real-world data. Upon completion, you’ll be able to use ARIMA models to forecast trends, assess financial risks, and optimize investment strategies. The course begins with an introduction to time series basics, exploring essential concepts such as stationarity, transformations, and autocorrelations. You’ll then dive into the specifics of financial time series, understanding their unique properties and learning how to apply ARIMA (AutoRegressive Integrated Moving Average) models. We cover both theoretical and practical aspects, ensuring you not only grasp the concepts but also gain hands-on experience through coding. You will go through detailed sections on ARIMA, starting with autoregressive and moving average models before progressing to the complete ARIMA framework. You'll explore the significance of ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) in model selection. Through practical coding examples, you'll learn how to implement these models, including Auto ARIMA and SARIMAX, and apply them to stock returns and sales data for forecasting. This course is ideal for anyone looking to advance their financial analysis skills, from analysts and investors to data scientists. A background in basic programming and financial concepts is recommended, but not required. With its intermediate difficulty level, the course offers a comprehensive learning experience for those interested in quantitative finance, machine lea