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 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
The proliferation of high-frequency data and advanced forecasting models across various domains has brought the implications of temporal granularity to the forefront of AI and statistical research.
Sophisticated readers should care because optimizing temporal granularity is crucial for reliable AI-driven decision making and resource allocation, particularly in fields like finance, logistics, and climate modeling where forecasting accuracy has significant real-world consequences.
This research provides a formal framework for understanding and mitigating the 'Granularity Paradox,' allowing for more robust and accurate time-series forecasting across different aggregation levels rather than relying solely on in-sample performance.
- · AI/ML researchers and developers
- · Quantitative analysts
- · Logistics and supply chain management
- · Financial modeling and trading
- · Organizations relying solely on in-sample metrics for time-series model validati
- · Traditional forecasting methodologies ignoring temporal aggregation effects
Improved accuracy of predictive models in various industries leading to better operational efficiency and risk management.
Development of new AI architectures and statistical methods specifically designed to handle 'Granularity Paradox' effectively, leading to more resilient autonomous systems.
Enhanced confidence in long-range forecasts could influence national strategic planning and resource distribution, especially in areas like energy and population dynamics.
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Read at arXiv cs.AI