arXiv:2606.18729v1 Announce Type: cross Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains such as healthcare, finance, and industrial monitoring, effective valuation methods are essential yet fundamentally lacking. Existing approaches are either model-dependent, limiting their generalizability, or designed for i.i.d. data and thus fail to capture temporal dependencies, multi-scale patterns, and non-stationary dynamics inherent to sequential data. We introd
The increasing complexity and scale of AI applications, particularly in critical real-time domains, necessitate principled data valuation methods that transcend current limitations.
Improving data valuation for time series data addresses a fundamental bottleneck in developing robust and generalizable AI, critical for critical infrastructure, finance, and healthcare.
This research introduces a model-agnostic approach to data valuation for time series, overcoming the limitations of previous i.i.d.-focused or model-dependent methods.
- · AI researchers
- · Healthcare sector (predictive analytics)
- · Financial institutions (algorithmic trading, risk management)
- · Industrial monitoring companies
- · Inefficient data collection practices
- · Model-dependent data valuation methods
More reliable and robust AI models in time-series critical applications.
Accelerated development and deployment of autonomous systems that rely on accurate real-time data.
Potential for new data-centric businesses focused on valuing and curating time-series datasets.
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Read at arXiv cs.LG