An Explanatory CDF Manifold Algorithm for Large Telecom Datasets

In this paper we introduce the CDF manifold algorithm, which operates on datasets where a single target dimension is strictly increasing given a minimum of two or more input dimensions, which is very common in telco data. The manifold can then be used to compute the closest upper and lower limits to a given new point, as well as its CDF. Training takes O(n.ln[n]) steps in the best case and O(n3/2) in the worst case. Lookup takes O(ln[n]) steps in the best case and O(n1/2ln(n)) in the worst case. The asymptotic computational cost is proven with a theorem. We compare our manifold method versus a standard dense neural network and show the asymptotic advantages both in terms of speed and accuracy. We also address potential speed gains through the use of reference points. In summary, the manifold is a non-parametric explanatory method to find the tightest data-driven upper and lower limits of the output dimension given a new unseen input. This makes it ideal for planning new site deployments where we need to find actual measurements as a baseline performance.