A Practical Approach to Timeseries Forecasting Using Python - Time Series Parameters

The video tutorial covers four key parameters: correlation, MAP, MAE, and RMSE. It explains how to calculate correlation between data columns using pandas, and how to compute MAP and MAE using the sklearn library. The tutorial also demonstrates the calculation of RMSE using Python's math functions. The module concludes with a summary and a preview of upcoming topics, including machine learning applications in time series analysis.

What is the difference between Mean Absolute Error (MAE) and Mean Squared Error (RMSE)?

How do you calculate RMSE using Python?

What libraries are mentioned for data visualization and machine learning in the text?