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I ran the Nearest Neighbor algorithm on Darwin Anomalies and Darwin Delta Anomalies (Darwin(t+1) - Darwin(t)). Meaning took a sub-interval of Darwin Delta Anomalies and found the nearest neighbor for it comparing it to other sub-intervals of same length.
And did so using 8 different metrics.
It seemed the algorithm would agree on a particular nearest neighbor across metrics for Darwin Delta Anomalies in specific the Correlation Distance and Cosine Distance agreed on 5+ nearest neighbors!
This is so since the mean for the Darwin Delta Anomalies is almost 0, therefore the Correlation Distance approaches towards Cosine Distance for Darwin Delta Anomalies.
What does this mean?
Correlations for Darwin Delta Anomalies then is geometrical vs. statistical. Therefore the Correlations for Darwin Delta Anomalies could be classified being within a certain cone (of tolerance). Outside the cone then there is no correlation, inside the cone there is high correlation.
Therefore, possibly, a cone could be define for strong El-Nino, the vectors within the cone correlate to the strong El-Nino and outside no correlation.
Other data of similar or dis-similar nature could be padded to intervals ofDarwin Delta Anomalies and as long as the mean is close to 0, this code classification would hold.
It might provide a better equation form for the modeling of El-Nino which would be an angle < tolerance .