Chapter 9: Exploratory Data Analysis and Diagnosis

So far, we have covered techniques to extract data from various sources. This was covered in Chapter 2, Reading Time Series Data from Files, and Chapter 3, Reading Time Series Data from Databases. Chapter 6, Working with Date and Time in Python, and Chapter 7, Handling Missing Data, covered several techniques to help prepare, clean, and adjust data.

You will continue to explore additional techniques to better understand the time series process behind the data. Before modeling the data or doing any further analysis, an important step is to inspect the data at hand. More specifically, there are specific time series characteristics that you need to check for, such as stationarity, effects of trend and seasonality, and autocorrelation, to name a few. These characteristics that describe the time series process you are working with need to be combined with domain knowledge behind the process itself.

This chapter will build on what...