We have several time series, which are basiclly chunks of numeric values.
We use Dynamic Time Warping to calculate the distance between these time series. This is working well and gives us some distances like 75.397 or 3752.34.
Our goal is to have somekind of a threshold to determine when two series are "similar". For our test cases (where we know the data) this is currently a fixed threshold like 100.0, where two series are similar when the distance is less than 100.0.
However, since we don't know the actual data and bounds of time series in the future, we cannot assume a fixed threshold for every case.
Our idea for now is to use some probability between 0 and 1 instead, so we can say "these two series have a similarity of 92%".
Is there any common method to map arbitrary distances to such a probability?
Here is an image of a time series:
This example shows 7 of those chunks. We now want to check the similarity of two of them, e.g. chunk one and chunk three...