Recurrence quantification analysis

Recurrence quantification analysis (RQA) is a fancy way of studying patterns that repeat themselves over time. It is used to help scientists and researchers understand time series data, which is basically just a bunch of measurements taken over a period of time.

To understand RQA, imagine you have a toy car that moves back and forth over a track. Every few seconds, you take a measurement of how far the car has moved from its starting point. You keep taking measurements for a few minutes, and then you stop the car.

Now, you have a bunch of data points that tell you where the car was at different times during its journey. But what can you do with that data? That's where RQA comes in.

RQA takes your data points and turns them into something called a "recurrence plot." This plot is like a map that shows how often the car returns to certain positions on the track. If the car keeps returning to the same position over and over again, that means there's a pattern in its movement. If it moves randomly and never returns to the same position, that means there's no pattern.

But that's not all RQA does. It also gives you some useful numbers that describe the patterns in your data. For example, it can tell you how long the car stayed in certain positions, how often it returned to those positions, and how closely spaced those returns were.

All of this information can help you figure out what's going on in your time series data. It can help you find patterns, detect anomalies, and make predictions about the future.

So, in summary, RQA is a tool that helps people study patterns in time series data. It turns data points into a recurrence plot and gives you useful information about the patterns in your data.