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

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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!

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🍿 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

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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.

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