> statistically speaking

That's a very big word you're using there for what is basically making shapes out of clouds. A bell-curve is the amortised function of a random variable with a mean and standar deviation. What does that have to do with a timeseries dataset?

A bell curve is not an "amortised function." Amortization applies to accounting and algorithmic time complexity, not probability distributions. You're likely thinking of a Probability Density Function (PDF). If you are going to police terminology, it helps to use the correct words. Second, fitting a curve with an R^2 of 0.911 is the exact opposite of "making shapes out of clouds.

> A bell-curve is the amortised function of a random variable with a mean and standard deviation.

The general notion of a bell-shaped curve is broader than that. Wikipedia has a reasonable overview: https://en.wikipedia.org/wiki/Bell-shaped_function

> “typically continuous or smooth, asymptotically approach zero for large negative/positive x, and have a single, unimodal maximum at small x.”