Though we’ve focused on tabular data so far in the book, another type of data you may often work with, and may need to perform outlier detection with, is time-series data. Time-series data is useful to look at in itself but is also a good example of an important concept in outlier detection: converting data from one format to another. Often, though certainly not always, tabular data can be treated as time series, and time-series data can usually be converted to table format. In general, with outlier detection, any item we examine may be typical in most ways but may, nevertheless, be unusual (and possibly unusual in an interesting way) in one or more other respects. To find these anomalies, we need to look at the data from different perspectives, and an important method to support this is converting data to another format. Although we’ll look specifically at time-series data, this concept can be extended to other types of data as well. First, though, we’ll take a closer look at what time-series data is and how it relates to the tabular data we’ve looked at so far.