Chapter 7: Handling Missing Data
As a data scientist, data analyst, or business analyst, you have probably discovered that obtaining a perfect clean dataset is too optimistic. What is more common, though, is that the data you are working with suffers from flaws such as missing values, erroneous data, duplicate records, insufficient data, or the presence of outliers in the data.
Time series data is no different, and before plugging the data into any analysis or modeling workflow, you must investigate the data first. It is vital to understand the business context around the time series data to detect and identify these problems successfully. For example, if you work with stock data, the context is very different from COVID data or sensor data.
Having that intuition or domain knowledge will allow you to anticipate what to expect and what is considered acceptable when analyzing the data. Always try to understand the business context around the data. For example, why is the data...
