2. Missing Data in Time Series#
Real-world time series data is rarely perfect. Sensors malfunction, measurements are skipped, systems go offline, and manual data entry introduces errors. This chapter addresses one of the most persistent challenges in time series analysis: dealing with missing data effectively.
In this chapter, you will learn the following topics:
Types of Missing Data: Understanding why data is missing is the first step toward handling it appropriately. This section explores three fundamental categories of missing data: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). You will learn how each type differs in its assumptions and implications for analysis, discover methods to test whether your data meets MCAR criteria, and understand how the type of missingness influences the choice of analytical methods. By recognizing which pattern your data follows, you can make informed decisions about whether simple deletion is acceptable or if more sophisticated imputation techniques are necessary.
Identifying Patterns of Missingness: Before you can address missing data, you must first identify where and how frequently it occurs. This section provides practical techniques for detecting missing values using Python tools like pandas and visualizing their distribution across time. You will learn how to quantify missingness at different temporal scales (daily, monthly, yearly), create visual representations such as heatmaps and scatter plots to reveal temporal patterns, and discover whether missing values cluster during specific periods or appear randomly throughout your time series. These diagnostic tools are essential for understanding the nature of your data gaps and informing your choice of handling strategy.
Imputation and Handling Strategies: Once you understand the patterns and types of missing data in your time series, the next question is how to handle it. This section covers a range of approaches from simple to complex, including deletion methods, forward-fill and backward-fill approaches, statistical imputation techniques, and advanced methods like interpolation and machine learning-based imputation. You will learn the trade-offs of each approach, when each is most appropriate given your data characteristics and analysis goals, and how to implement these strategies in practice. The section emphasizes that there is no one-size-fits-all solution; the best approach depends on the amount of missing data, the type of missingness, and the objectives of your analysis.