Chapter 12
Analyzing Blockchain Data over Time
IN THIS CHAPTER
Examining how behavior changes over time
Looking beyond seasonal trends
Learning from data cycles
Identifying trends despite fluctuations
Writing Python code to analyze time series data
You learn about identifying clusters of data in Chapter 9, how to predict an object’s classification in Chapter 10, and how to predict future activity with regression in Chapter 11. All three chapters examine data as monolithic datasets. That’s not bad but looking at data as being the same doesn’t always help to tell the whole story. In many cases, your data depends on time. For example, sales occur at specific times on specific days. Each transaction’s timestamp may be hiding its own keys to understanding your data.
If you’ve ever used a coffee shop as an alternate office, you’ve probably noticed that sometimes it's busy and other times it's not. In the case of any retail business, time affects employee workload and should affect a business’s ...
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