Financial Analysis with ARIMA and Time Series Forecasting

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

9 hours 49 minutes
Sessions

On-Demand
Subtitles

English

Found in

Overview

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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 learning, and time series forecasting.

Syllabus

Welcome

In this module, we will introduce the course structure and objectives, providing an overview of the key content and what you can expect. We will also highlight a special offer available to learners, outlining how it enhances the learning experience. This section ensures you are equipped with all the necessary details before diving into the course material.

Getting Set Up

In this module, we will guide you through the optional warm-up exercise to get familiar with the course environment. You will also learn where to access and download the code needed for the course, ensuring you have everything in place to start coding. This section is essential for setting up your workspace for a smooth learning experience.

Time Series Basics

In this module, we will introduce the foundational concepts of time series analysis, explaining what it is and how it’s used. We will also explore the distinction between modeling and predicting, and cover essential transformations to improve your data. Finally, you will gain insights into enhancing your analysis with feedback and suggestions.

Financial Basics

In this module, we will cover the core principles of financial time series, providing you with a solid foundation. You’ll learn about random walks and the Random Walk Hypothesis, which play a critical role in financial modeling. Additionally, we will explore the concept of naive forecasting and why establishing baselines is essential for accurate predictions in finance.

ARIMA

In this module, we will dive deep into the ARIMA model, exploring its components like AR(p) and MA(q), and understanding how to apply it for time series forecasting. You will also learn to identify stationarity, compute ACF and PACF, and use Auto ARIMA for model selection. We will provide hands-on coding examples for various data types, allowing you to practice forecasting with ARIMA in real-world scenarios.

Setting Up Your Environment (Appendix)

In this module, we will guide you through the process of setting up your development environment. You'll first perform a pre-installation check to ensure everything is in place, then set up Anaconda to manage your dependencies. Finally, we will show you how to install key libraries needed for the course, including Numpy, Scipy, and TensorFlow, so you can start working on hands-on projects right away.

Extra Help With Python Coding for Beginners (Appendix)

In this module, we will provide extra support for beginners by covering the basics of coding and how to become more confident in writing your own code. You will learn how to effectively use Jupyter Notebook, with a demonstration of its advantages. Additionally, we will introduce you to GitHub and offer optional coding tips to enhance your learning and project management.

Effective Learning Strategies for Machine Learning (Appendix)

In this module, we will share strategies to maximize your success in this course, offering insights into the best learning approaches based on your experience level. You will also assess the course’s suitability for your background and determine whether to follow an academic or practical path. Finally, we’ll guide you on the best order to take related courses to enhance your machine learning journey.