Time series analysis is a statistical technique used to analyze and model time-based data to extract meaningful insights and identify patterns. It is widely used in fields such as finance, economics, and engineering to analyze trends, make forecasts, and build predictive models.

In Python, there are several libraries available for performing time series analysis, including:

1. Pandas: This library provides fast, flexible, and expressive data structures and data analysis tools, including time series data handling and manipulation.

2. Numpy: This library is used for numerical computations, including the manipulation of arrays and matrices, which are essential for time series analysis.

3. Matplotlib: This library provides tools for data visualization, including line plots, histograms, and scatter plots, which are useful for visualizing time series data and results.

4. Statsmodels: This library provides tools for statistical analysis, including time series analysis. It includes functions for statistical modeling, hypothesis testing, and estimation.

5. Prophet: This library is an open-source tool for time series forecasting that is built on top of the PyStan library. It provides an easy-to-use interface for making predictions based on historical data.

When performing time series analysis, you typically start by cleaning and preprocessing your data, followed by plotting the data to visualize any trends or patterns. You then use statistical techniques to model the time series data and make predictions about future values. The choice of method and model will depend on the specific problem you are trying to solve and the characteristics of your data.