enhancementgood first issuenew outcome space
Repository metrics
- Stars
- (79 stars)
- PR merge metrics
- (PR metrics pending)
Description
Describe the feature you'd like to have
This paper introduces the "amplitude entropy". As for many of the other methods, this isn't any new entropy, but a new OutcomeSpace. Specifically, an input time series x is transformed as follows:
|z(𝑡)| = √︁(𝑥(𝑡)^2 + H{𝑥(𝑡)}^2)
where H{𝑥(𝑡)} is the Hilbert transform of the time series x(t).
They then bin the amplitude time series, normalise the histogram to form a a set of probabilities (i.e. use RelativeAmount), then compute Shannon entropy.
If possible, sketch out an implementation strategy
This should just be implemented as a new OutcomeSpace, and mention in the doctoring that it was originally used as part of the "amplitude entropy".