# Getting Started with Time Series

In this chapter, we introduce the main concepts and techniques used in time series analysis. The chapter begins by defining time series and explaining why the analysis of these datasets is a relevant topic in data science. After that, we describe how to load time series data using the `pandas`

library. The chapter dives into the basic components of a time series, such as trend and seasonality. One key concept of time series analysis covered in this chapter is that of stationarity. We will explore several methods to assess stationarity using statistical tests.

The following recipes will be covered in this chapter:

- Loading a time series using
`pandas`

- Visualizing a time series
- Resampling a time series
- Dealing with missing values
- Decomposing a time series
- Computing autocorrelation
- Detecting stationarity
- Dealing with heteroskedasticity
- Loading and visualizing a multivariate time series
- Resampling a multivariate time series
- Analyzing the correlation among pairs of variables

By the end of this chapter, you will have a solid foundation in the main aspects of time series analysis. This includes loading and preprocessing time series data, identifying its basic components, decomposing time series, detecting stationarity, and expanding this understanding to a multivariate setting. This knowledge will serve as a building block for the subsequent chapters.