Welcome to How to build ARIMA models in Python for time series forecasting. You'll build ARIMA models with our example dataset, step-by-step. By following this tutorial, you’ll learn: 00:00 What is ARIMA (definition) 04:55 Step 0: Explore the dataset 06:28 Step 1: Check for stationarity of time series 12:25 Step 2: Determine ARIMA models parameters p, q 14:40 Step 3: Fit the ARIMA model 15:07 Step 4: Make time series predictions 16:30 Optional: Auto-fit the ARIMA model 18:15 Step 5: Evaluate model predictions 19:30 Other suggestions If you want to use Python to create ARIMA models to predict your time series, this practical tutorial will get you started. GitHub Repo with code and dataset: Technologies that will be used: ☑️ JupyterLab (Notebook) ☑️ pandas ☑️ numpy ☑️ statsmodels ☑️ matplotlib ☑️ pmdarima ☑️ sklearn Links mentioned in the video ► documentation: To learn Python basics, take our course Python for Data Analysis with projects: There's also an article version of the same content. If you prefer reading, please check it out. How to build ARIMA models in Python for time series prediction: Get access to more data science materials, check out our website Just into Data: