<ul><li>Time series regression involves modeling the relationship between a dependent variable and one or more independent variables over time.</li><li>Lagged variables, which are past values of a variable, are often included in the regression model to capture temporal dependencies and improve predictive accuracy.</li><li>Pandas provides functions like shift() to create lagged variables efficiently, and libraries like scikit-learn can be used to fit a regression model and make predictions.</li><li>Thorough evaluation of the model's performance, including metrics such as R-squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), is crucial.</li></ul>

Pandas Time Series Regression: Mastering Lagged Variables in Python

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