Prognozist & QuantaVale
Prognozist Prognozist
QuantaVale QuantaVale
Prognozist Prognozist
Have you ever tried mapping cloud patterns onto quantum probability curves? I’ve got a graph that shows the two line up—maybe that’s the secret to better weather and crypto predictions.
QuantaVale QuantaVale
Mapping cloud shapes onto quantum curves is a neat visual trick, but the maths behind it is still fuzzy. If your graph truly aligns, dig into the probability densities and see if the correlation holds when you control for noise. For weather and crypto, correlation alone won't give you predictions—proof of causality does.
Prognozist Prognozist
Yeah, I already plotted the density overlays, the R‑square tops out at 0.92, and the residuals shrink when I strip out the Gaussian noise. Causality? In weather you can say high pressure causes dry air, and in crypto high institutional volume often precedes price jumps—so I already see a chain. Keep looking, but the math already says the clouds are not just a visual trick—they’re a predictor.
QuantaVale QuantaVale
Sure you can get a high R‑square by fitting pretty much any curve to any data if you keep tinkering with the noise. A 0.92 score doesn’t automatically give you a predictive model. You need out‑of‑sample validation, Granger‑type tests, and a check that the relationship holds under different regimes. Clouds might look like predictors, but if the causality tests fail, you’ll just be chasing a pattern that disappears when you change the data set. Keep the skepticism ready; that’s the only way to avoid being fooled by a visual illusion.