Evaluation for Regression Analyses on Evolving Data Streams

TL;DR

This paper addresses the challenges of regression analysis in evolving data streams by proposing a standardized evaluation process and a novel drift simulation strategy, validated through extensive experiments with state-of-the-art methods.

Contribution

It introduces a standardized evaluation framework and a new drift simulation method for regression in streaming data, including incremental drift types.

Findings

The evaluation process effectively assesses regression methods in streaming contexts.

The drift simulation strategy can generate diverse drift scenarios, including incremental drift.

Experimental results demonstrate the robustness of the proposed approach.

Abstract

The paper explores the challenges of regression analysis in evolving data streams, an area that remains relatively underexplored compared to classification. We propose a standardized evaluation process for regression and prediction interval tasks in streaming contexts. Additionally, we introduce an innovative drift simulation strategy capable of synthesizing various drift types, including the less-studied incremental drift. Comprehensive experiments with state-of-the-art methods, conducted under the proposed process, validate the effectiveness and robustness of our approach.