> "Those corners must be your anti-entropy filtering. "

No, it's just kindergarten-level connect the dots

> "Nonlocal-in-time"

It has some commonality to a non-stationary time-series.

Take a look at this YT video that I created several weeks ago. The wave appears to be moving to the right but then reverses direction. It thus appears to be non-stationary.


More clarification here where I show how my LTE formulation allows reversing traveling waves. :


My thinking is that what is being ascribed to a chaotic wave behavior is more likely just a consequence of non-linear math in Navier-Stokes that has not been fully explored. None of the conventional approaches such as Fourier series will work on these kinds of waveforms, which is why many people looking at real time-series hit a dead-end and just assume it's a chaotic property.


Today I was cleaning up a massive model comparison between the Pacific ocean's ENSO time-series and the Atlantic Ocean's AMO time-series. My premise is that the tidal forcing is essentially the same in the two oceans, but that the standing-wave configuration differs. So the approach is to maintain a common-mode forcing in the two basins while only adjusting the Laplace's tidal equation (LTE) modulation.

If you don't know about these completely orthogonal time series, the thought that one can fit even one, let alone two time-series simultaneously is unheard of (Michael Mann doesn't even think that the AMO is a real oscillation based on reading his latest research article).

This is the latest product


Read this backwards from H to A.

H = The 2 tidal forcing inputs for ENSO and AMO -- differs really only by scale and offset

G = The constituent tidal forcing spectrum comparison of the two -- primarily the expected main constituents of the Mf fortnightly tide and the Mm monthly tide, amplified by an annual impulse train which creates a repeating Brillouin zone.

E&F = The LTE modulation for AMO, essentially one strong high-wavenumber modulation as shown to the right

C&D = The LTE modulation for ENSO, a strong low-wavenumber that follows the El Nino La Nina cycles and then a higher modulation

B = The AMO fitted model modulating H with E

A = The ENSO fitted model moduating H with C

Ordinarily, this would take eons of machine learning compute time to determine, but with knowledge of how to solve Navier-Stokes, it's a tractable problem.