PACF Explained: A Simple Guide to Partial Autocorrelation with Python

🧠 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-aut... Struggling to understand how much past values influence your current data in a time series? This tutorial breaks down Partial Autocorrelation (PACF) in simple terms—and shows you how to compute and visualize it using Python with just a few lines of code! Code: https://ryanandmattdatascience.com/pa... 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *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:    • Mastering Time Series Analysis in Python   PACF:    • PACF Explained: A Simple Guide to Partial ...   ADF Test:    • How to Perform the Augmented Dickey-Fuller...   KPSS Test:    • KPSS Test Explained: Check Time Series Sta...   In this comprehensive time series tutorial, I break down the partial autocorrelation function (PACF) and show you exactly how to use it for analyzing time series data. We start by exploring what PACF actually measures and how it differs from the regular autocorrelation function (ACF). I explain the key concepts behind PACF, including how it isolates direct correlations between lags while removing intermediate effects, making it essential for identifying autoregressive model orders. You'll learn how to interpret PACF plots, understand significance bounds and confidence intervals, identify cutoff points, and recognize patterns that indicate different time series behaviors. The video includes real examples using Apple stock closing prices, demonstrating both non-stationary and stationary data scenarios. I walk through the entire Python implementation using just a few lines of code with pandas, numpy, matplotlib, and statsmodels. You'll see how to load data, prepare it for analysis, apply log transformations and differencing, and create clear PACF visualizations with customizable parameters. By the end of this tutorial, you'll understand when to use PACF versus ACF, how to read PACF plots to determine appropriate AR model orders, and be able to implement PACF analysis in your own time series projects with confidence. *GitHub code and dataset links in the description below.* TIMESTAMPS 00:00 Introduction to PACF 00:42 What is Autocorrelation? 01:30 Understanding PACF Formula 02:10 Interpreting PACF Values and Significance Bounds 03:22 Cutoff Points in PACF 04:09 Slow Decay and Seasonality 04:34 Differences Between ACF and PACF 05:10 Python Programming Setup 06:15 Loading Stock Data 07:00 Plotting Non-Stationary PACF 08:12 Creating Stationary Data 09:20 Plotting Stationary PACF 10:20 Analyzing Results and Wrap-up OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / 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.