Time series data, sequences of observations ordered by time, appears frequently in various domains, from finance and economics to weather patterns and sensor readings. Unlike standard cross-sectional data, the temporal ordering is a key feature, meaning observations $y_t$ are often dependent on previous values such as $y_{t-1}$.

This chapter introduces the foundational concepts needed to work with this type of data. We will cover:

• Identifying the unique characteristics and components like trend and seasonality often present in time series.
• Using the Pandas library in Python to effectively load, handle, and manipulate time-indexed data.
• Techniques such as time shifting, calculating lags, and applying rolling window operations.
• Basic methods for visualizing time series data to gain initial insights into its structure.

By the end of this chapter, you will be equipped with the basic tools to start exploring and preparing time series datasets for analysis.