The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
Article summary
Quick briefing — cleaned from the original RSS feed
arXiv:2607.05450v1 Announce Type: new Abstract: This paper explores the "Granularity Paradox" in time-series forecasting, wherein finer temporal disaggregation (e.g., Monthly to Weekly/Daily) improves in-sample diagnostics and dataset size (N), but degrades out-of-sample accuracy due to recursive error compounding over longer horizons (H). Conversely, coarse aggregation (Annual) eliminates recursive error propagation but reduces data available to estimators. We formalize this trade-off and…
1Key Takeaways
- Conversely, coarse aggregation (Annual) eliminates recursive error propagation but reduces data available to estimators.
- Headline: The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
- Category focus: Research — relevant for AI builders and decision-makers.
2AIWedia Score
10/10
Must-read — high impact for AI builders
Based on source trust, recency, category impact, and story depth.
3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that conversely, coarse aggregation (Annual) eliminates recursive error propagation but reduces data available to estimators.
Explore related
Browse toolsRelated tools
Research news
Explore curated research tools on AIWedia — compare, rank, and launch from our directory.
Full story on arXiv ML
Read full articleHeadlines aggregated via RSS for discovery on AIWedia. Original content © arXiv ML. We link to the source and do not republish full articles.