Differentiate between predicting when and where a tornado will strike and predicting levels of abnormal tornadoes to increase during a season. As I wrote to you before, what seems chaotic to you, is a long-term cyclical pattern to those who have input all the data and had the computer correlate. Armstrong has done that. And he continues to be accurate on all of his predictions. Those who haven't taken the time to study his record, are going to be the big losers.
"Violent storms" says nothing about aggregate damage levels. I am amazed at how poor the English comprehension of so many readers. Perhaps it is because my reading comprehension is near to the 100th percentile.
You continue to force me to repeat what I had written to you earlier. That the short-term predictions are much more noisy. Historical cycles predict the long-term turns very accurately as Armstrong has emphatically and unequivocally proven! (if you haven't studied his record since 1985 or so, then you are ignorant of this proof)
Again you make me repeat what I wrote to your before, that what appears to be unpredictable to those who haven't model sufficient historical data to establish the long-term cycles, is not random to those who have collected the necessary data.
If you study Taleb's anti-fragility math (which followed his Black Swan work), it is clear that systems overcommit to error because they lack a holistic perspective.
Taleb is pointing out that they are unseen and that is what makes them Black Swans, but he is saying the reason they are unseen and unpredictable is because the system is structured in such a way that makes it incapable of incrementally adjusting to error. He makes two essential equivalent points (they are duals). One is that bottom-up systems have more degrees-of-freedom and the agents are closer to the error and can adjust more readily (this is the simulated annealing point I have been making over and over again since at least 2010). Secondly, implicit in his math is that if there is a historically repeating pattern then this information would not prevent top-down systems from being unable to adjust to the error that the information makes clear.
I understand you are incapable of understanding.
I ignore pontifications from the ignorant.
Again differentiate between modeling every short-term event and modeling cyclical patterns of aggregate activity.
For example, the cyclical movement of the earth's north and south magnetic poles, which impact aggregate weather patterns.
Point is that fundamental analysis (e.g. climatologists) isn't necessary to recognize repeating patterns which be correlated without any fundamental knowledge. And it turns out that fundamental analysis is often very poor at making predictions, because it fails to account for some factors, e.g. Greenspan not accounting for international capital flows.