Stationarity of Time Series | Time Series Forecasting - ARIMA | Skillfloor

Описание: Is your time series data ready for forecasting? Let’s find out why stationarity matters before you build your ARIMA model!

In this video, we break down the concept of stationarity in time series and explain why it’s a key step before using forecasting models like ARIMA. You’ll learn how to identify stationary data, perform tests like the ADF test, and understand how differencing transforms non-stationary data into a stationary form. Perfect for beginners and data enthusiasts who want to build accurate forecasts.

📚 What you’ll learn:

1. What is stationarity, and why is it important
2. Types of stationarity
3. How to test for stationarity (ADF test, plots)
4. Steps to make data stationary for ARIMA models

🎥 Watch till the end to see a simple, hands-on example with real data!

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