The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

A study on the 'Granularity Paradox' in time-series forecasting finds that finer temporal disaggregation improves in-sample fit but degrades out-of-sample accuracy due to recursive error compounding. Benchmarking 10 models across six granularities on a 13-year public procurement dataset, the LSTM shows a U-shaped error curve, while linear regression remains stable. The research introduces a consensus-dissensus diagnostic to identify models where standard metrics mask cumulative error propagation.

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 benchmark 10 models - spanning na\"ive, statistical, machine learning, and deep learning architectures - across six granularities using a 13-year public procurement dataset. The empirical results reveal a non-monotonic threshold structure: recursive autoregressive and seasonal models degrade substantially under high-frequency forecasting e.g., Holt-Winters reaches a Test R-squared of -151 and TPFE of 425.85% at the Daily grain , while the LSTM traces a U-shaped error curve, worsening from Monthly 19.66% through Bi-Weekly 35.94% before overcoming the error propagation penalty at Daily TPFE of 4.35%, R-squared of 0.66 . Linear Regression remains stable across all granularities 16.3-17.0% TPFE , confirming that the paradox is driven by recursive feedback topology, not model complexity. The results demonstrate that standard pointwise metrics RMSE, MAE systematically mask cumulative error propagation, and that evaluating forecasts without goal-dependent cumulative metrics produces misleading assessments of model adequacy. We introduce a consensus-dissensus diagnostic comparing the directional behaviour of pointwise metrics against cumulative TPFE across granularities, enabling the identification of models whose standard diagnostics mask systematic error propagation.