Temporal-Decay Shapley: A Time-Aware Data Valuation Framework for Time-Series Data

TL;DR

This paper introduces a time-aware data valuation framework for time-series data, utilizing temporal decay mechanisms and multi-scale fusion to improve accuracy in noise detection and data importance assessment.

Contribution

It proposes three enhanced temporal Shapley methods that incorporate temporal decay and multi-scale strategies for more accurate time-series data valuation.

Findings

Methods outperform traditional data valuation in noise detection.

Enhanced methods show robustness under various temporal settings.

Multi-scale fusion balances short-term and long-term data importance.

Abstract

With the rapid development of machine learning applications on time-series data, accurately assessing the value of training samples has become essential for data selection, noise detection, and model optimization. However, traditional data valuation methods usually assume that samples are independent and identically distributed, and thus ignore the time-varying nature of sample value in time-series data. This paper proposes an improved temporal Shapley data valuation method that enables accurate sample valuation for time-series data through a temporal decay mechanism and a multi-scale fusion strategy. Specifically, we propose three progressively enhanced temporal Shapley methods. Temporal-Decay Shapley (TDS) incorporates temporal information into Shapley value computation through exponential decay weights; the improved TDS adopts power exponential decay to better adapt to nonlinear temporal drift; and Multi-Scale Temporal-Decay Shapley (MS-TDS) constructs a multi-scale fusion mechanism that balances the value of short-term hotspot samples and long-term foundational samples through parallel multi-scale valuation and sample-level adaptive fusion. Experimental results show that the proposed methods generally outperform traditional methods in noise detection and high-value data identification tasks, with more evident advantages under most strongly temporal settings, thereby effectively improving the accuracy and robustness of data valuation.