Pandas Time Series

In this project, you will learn how Pandas handles dates and times by working with a **light vehicle sales dataset** from FRED (Federal Reserve Economic Data). You will not build any model; the focus is entirely on understanding time series data structures and the Pandas tools that come with them.

1. Load the vehicle sales CSV from [FRED](https://fred.stlouisfed.org/series/LTOTALNSA) into a Pandas DataFrame
2. Parse the date column and set it as the index
3. Create a date range using `pd.date_range()` and understand frequency strings (`D`, `W`, `M`)
4. Resample the data to monthly and quarterly totals
5. Shift the series by one period and compute period-over-period differences
6. Plot the original series and the rolling mean on the same chart