Smoothing, aggregation, and binning
In our discussion about noise in data in Chapter 11, Data Cleaning Level III – Missing Values, Outliers, and Errors, we learned that there are two types of errors – systematic errors and unavoidable noise. In Chapter 11, Data Cleaning Level III – Missing Values, Outliers, and Errors, we discussed how we deal with systematic errors, and now here we will discuss noise. This is not covered under data cleaning, because noise is an unavoidable part of any data collection, so it cannot be discussed as data cleaning. However, here we will discuss it under data transformation, as we may be able to take measures to best handle it. The three methods that can help deal with noise are smoothing, aggregation, and binning.
It might seem surprising that these methods are only applied to time-series data to deal with noise. However, there is a distinct and definitive reason for it. You see, it is only in time-series data, or any data that...
