Behavioral structures exist within timeseries signals, but they are implicit and often not accessible to other computational entities.
Here’s an example. You approach me as a researcher. You have a signal, and you know more or less what the signal does. It repeats, it increases here and decreases there. When it enters state x, another signal enters state y.
You have questions like: how long was I in this state or that? How long did it take me to reach threshold a or value b? You don’t know, because all you have is data. So here we are.
In our study, we assume that the signal in composed of repeating states. In a wave, we already understand the mechanics of how the states repeat. In your data, you already know what the states are.
You can think of signals like this:
each signal is composed of repeating states
we can mark where we are in a state
we can mark when we enter or exit a state
For an object like a wave, I can tell you what the significant states are. For another type of signal or set of signals, you can define for me what the states you care about are. Then we can create a document that defines those states, tells you when you enter and exit those states, and then you’re done. You have states and events.
Once you have states and events, you move on to measures. Measures are when we talk about what happend over the course of an event. How high did the value go? How long does it take the signal to repeat? The key is that we now see the signal as an oscillation, a repeating wave.
So now you have more data. It’s like magic; you don’t know where it came from, but now you know how long you were in state a and how long it took to reach state b, and you know how that’s changed over the past month or year. And the best part is, we used the same ten lines of code to give you 90% of those results.