Implementation of the recovering corrections into the intermittent data analysis

TL;DR

This paper introduces an enhanced method for analyzing intermittent data by incorporating bin-bin correlation information through a recursive algorithm, improving the accuracy of data recovery in multiplicative cascading models.

Contribution

It presents a novel recursive algorithm that integrates density correlators into intermittent data analysis, advancing beyond traditional methods.

Findings

The method effectively captures bin-bin correlations.

It improves data recovery accuracy in cascading models.

The approach is validated through model testing.

Abstract

The improved method of intermittent data analysis is proposed. It exploits, in addition to the standard density moments, the information on the bin-bin correlations, observed in the data and expressed in terms of the density correlators. The improving recovering corrections are implemented into the data analysis in the form of the recursive algorithm, and tested in the framework of multiplicative cascading models.