How to Perform the Augmented Dickey-Fuller (ADF) Test in Python скачать в хорошем качестве

How to Perform the Augmented Dickey-Fuller (ADF) Test in 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... Working with time series data? Before building a model, you need to know whether your data is stationary. In this tutorial, you’ll learn how to run the Augmented Dickey-Fuller (ADF) Test in Python using statsmodels—step by step. Code: https://ryanandmattdatascience.com/au... 🚀 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   KPSS Test:    • KPSS Test Explained: Check Time Series Sta...   Time Series Stationary:    • Understanding Stationary Data in Time Seri...   Seasonality:    • Identifying and Handling Seasonality in Ti...   In this comprehensive time series tutorial, we dive deep into the Augmented Dickey-Fuller (ADF) test, a crucial statistical test for determining whether your time series data is stationary. Understanding the ADF test is essential for anyone working with forecasting models like ARIMA, as stationarity is a key assumption for many time series models. We start by explaining the background of the ADF test, including the null hypothesis (data is non-stationary with a unit root) versus the alternative hypothesis (data is stationary without a unit root). You'll learn what stationarity really means, why it matters, and how unit roots affect your time series analysis. I walk through the test information, including how to interpret p-values with a significance level of 0.05, and explain what the test statistics actually tell you about your data. Then we jump into practical Python implementation using the statsmodels library. I demonstrate how to perform the ADF test on both stationary and non-stationary synthetic data, showing you exactly what results to expect in each scenario. You'll learn how to access the tuple results, extract the p-value and other critical statistics, and I share a custom function I use regularly that automates the interpretation process. This function makes it incredibly easy to quickly check stationarity as you transform your data through techniques like differencing or logarithmic transformations. By the end of this video, you'll confidently be able to test for stationarity in your own time series projects and make informed decisions about data preprocessing. TIMESTAMPS 00:00 Introduction to ADF Test 00:42 Background & Hypotheses 01:50 Test Information & P-Values 02:43 Python Implementation 04:51 Creating Sample Data 06:23 Understanding Test Results 08:37 Accessing Result Components 10:00 Building a Custom Function 12:40 Testing Stationary Data 13:10 Testing Non-Stationary Data 14:10 Wrap-up & Summary 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.