An Explanatory CDF Manifold Algorithm for Large Telecom Datasets

In this paper we introduce the CDF manifold algorithm that operates on data sets where a single target dimension is strictly increasing given a minimum of two or more number of input dimension which is very common in telco data. The manifold can then be used to compute the closest upper and lower limit to a given new point as well as its CDF. Training takes O(n.ln[n]) steps in the best case and O(n^3/2) in the worst case. Look up takes O(ln[n]) steps in the best case and O(n^1/2) in the worst case. The asymptotic computational cost is proven with a theorem. We compared our manifold method versus a standard dense neural network and show the asymptotic advantages both in terms of speed and accuracy. We also comment of potentials speed gains through the use of reference points. In summary, the manifold is a non-parametric and explanatory method to find the tightest data driven upper and lower limit of the output dimension given a new unseen input. This makes it ideal for planning new site deployments where we would need to find actual measurements as base-line performance.