Practical Time Series Analysis

Summary

In this chapter, we discussed several types of data such as cross-sectional, time series, and panel data. We delved into the special properties that make time series data special. Several examples and working code in Python have been discussed to give an understanding of how exploratory data analysis can be performed on time series to visualize its properties. We also described autocorrelation and partial autocorrelation and graphical techniques to detect these in a time series. The topics discussed in this chapter give us the stage for a more detailed discussion for working on time series data in Python. In the next chapter, you will learn how to read more complex data types in time series and use such information for more in-depth ...