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Time-series forecasting paradox revealed: finer data degrades accuracy

By PulseAugur Editorial · [1 sources] ·

A new paper introduces the "Granularity Paradox" in time-series forecasting, highlighting how increasing temporal disaggregation improves in-sample fit but degrades out-of-sample accuracy due to compounded errors. The research formalizes this trade-off and benchmarks ten models across six granularities using a 13-year public procurement dataset. Findings indicate that while some models like Holt-Winters perform poorly at daily frequencies, LSTMs show a U-shaped error curve, and Linear Regression remains stable, suggesting the paradox is linked to recursive feedback topology rather than model complexity. AI

IMPACT Highlights potential pitfalls in using standard metrics for evaluating complex AI models in time-series forecasting.

RANK_REASON Academic paper detailing a new paradox in time-series forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Time-series forecasting paradox revealed: finer data degrades accuracy

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Academic paper detailing a new paradox in time-series forecasting. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hugo Moreira ·

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

    arXiv:2607.05450v1 Announce Type: cross 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 accura…

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