Beginner's Guide to Autocorrelation (ACF) in Python for Time Series

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 Want to detect patterns and lags in your time series data? In this hands-on tutorial, you’ll learn what autocorrelation is, how to use the Autocorrelation Function (ACF), and how to apply it in Python using statsmodels and pandas. Perfect for beginners in time series forecasting, finance, or sensor data analysis! Code: https://ryanandmattdatascience.com/acf-autocorrelation-function/ 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Python Time Series Playlist: https://www.youtube.com/playlist?list=PLcQVY5V2UY4LEJM7WxpL-VCeh7m7yD9gb PACF: https://youtu.be/XstPVx78yi8 Time Series Box Cox: https://youtu.be/xd4zas1VWJw ADF Test: https://youtu.be/2cWsi6dfJ90 In this time series video, I break down the autocorrelation function (ACF) and show you exactly how to use it to analyze time series data in Python. The autocorrelation function is essential for understanding patterns in your time series data and determining whether your data is stationary or not. I start by explaining what autocorrelation means and how it measures the correlation of a time series with delayed copies of itself. Then I walk through how to read ACF plots, including understanding lag values, significance bounds, and the 95% confidence interval. You'll learn to identify three key patterns: gradual decay (indicating non-stationary data), repeated peaks (showing seasonality), and sharp cutoffs (suggesting stationary data suitable for autoregressive models). I also compare the ACF to the PACF (partial autocorrelation function) and explain when to use each one for model selection. The ACF helps with moving average model orders, while the PACF is useful for autoregressive model orders. In the practical coding section, I demonstrate how to plot an ACF in just a few lines of Python code using the statsmodels library. I use Apple stock closing prices as a real example and show you how to transform non-stationary data into stationary data using log transformation and differencing. By the end of this tutorial, you'll be able to confidently interpret ACF plots and apply them to your own time series forecasting projects. TIMESTAMPS 00:00 Introduction to Autocorrelation Function (ACF) 00:41 What is Autocorrelation? 01:50 Understanding the ACF Plot 03:47 Interpreting ACF Patterns 04:50 ACF vs PACF Comparison 05:26 Coding ACF in Python 07:43 Transforming to Stationary Data 09:54 Plotting Stationary ACF OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.