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This is the simplest algorithm for Machine Learning forecasts, in particular it looks for signal self-similarity and averages out the out from each similar case called Nearest-neighbour.
Similar error of 9% to SVR and NN is found.
All known and popular distance functions were tried, but the most accurate forecasts were obtained by non-Euclidean metrics.