SPIN Unprocessed July 8, 2026 ai_technology research
The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
View original on arxiv.orgOverview
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 bench
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- Safe Bayesian Optimization with Counterfactual Policies
- A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models
- SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
- EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation
- Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
- Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO