Futuristic Classification with Dynamic Reference Frame Strategy

TL;DR

This paper introduces a novel dynamic reference frame strategy to create predictive time windows, enabling organizations to act proactively on classification outcomes such as churn or faults.

Contribution

It proposes a new reference frame creation method that provides a predictive buffer time, addressing a key gap in existing classification techniques.

Findings

Effective in creating actionable time windows before events occur

Improves response time for churn and fault prediction

Enhances decision-making in real-time scenarios

Abstract

Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper