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title: "The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error — Stuff That Spins"
description: "arXiv:2607.05450v1 Announce Type: new Abstract: This paper explores the \"Granularity Paradox\" in time-series forecasting, wherein finer temporal disaggregation…"
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date: "2026-07-08T04:00:00+00:00"
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# The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

**Source:** Unknown  
**Published:** July 8, 2026  
**Original:** https://arxiv.org/abs/2607.05450  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

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

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