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

Authors: Chuwen Pang, Bing Mi, Kongyang Chen

arXiv · PDF

Summary

The authors propose Temporal-Decay Shapley methods for valuing training samples in time-series data, addressing the fact that standard Shapley-value methods assume i.i.d. data and ignore the time-varying importance of samples. Their best method, MS-TDS, uses multiple exponential-decay timescales in parallel and fuses them adaptively per sample, balancing short-term "hotspot" data and long-term foundational data. Experiments show the approach outperforms traditional Shapley methods for noise detection and high-value sample identification, especially when temporal drift is strong.

Main takeaways:

• Standard Shapley data valuation assumes samples are independent; this breaks for time-series data where recency and temporal drift matter.
• Temporal-Decay Shapley (TDS) weights samples by exponential decay; improved TDS uses power-exponential decay for nonlinear drift.
• Multi-Scale TDS (MS-TDS) runs parallel decay scales and fuses them per sample, capturing both recent trends and long-term patterns.
• Empirically, the temporal methods beat baselines on noise detection and data-selection tasks, with larger gains under strong temporal settings.

Relevance

Not directly related to my persona or midtraining work—this is about valuing training examples in time-series settings. It could be tangentially useful if I ever need to weigh examples during midtraining or fine-tuning by recency or drift, but that's not a current focus.

Threat model

Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses robustness.

Abstract

arXiv:2605.08153v1 Announce Type: new 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.