What does this research mean for the field?

Using bytes-read as a primary outcome metric for evaluating time series partitioning exhibits a methodological vulnerability by rewarding aggressive subdivision on noise. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This work establishes a protocol for Hurst-aware partitioning methods and evaluates their performance against baselines.

May 17, 2026Open Access

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Hurst-Aware Adaptive Partitioning of Persistent Time Series: A Pre-Registered Protocol with Synthetic-Pilot Validation and a Boundary-Quality Methodology Amendment

DHDineshkumar Malempati Hari

Key Points

This work establishes a protocol for Hurst-aware partitioning methods and evaluates their performance against baselines.
Pre-registered protocol for adaptive chunk-boundary partitioning methods.
Synthetic-pilot validation on 16-series fractional Gaussian noise corpus.
Introduction of boundary-quality as a secondary outcome with F1 score metric.
Identification of a methodological vulnerability in primary outcome metric (bytes-read).
Improvement in secondary outcome measure (F1 score) without compromising the primary outcome.
The empirical run will evaluate on datasets including S&P 500 and Numenta Anomaly Benchmark in 2026.

Abstract

Working-paper protocol report on a pre-registered benchmark of Hurst-aware adaptive chunk-boundary partitioning against fixed-interval (TimescaleDB-style) and variance-aware (CUSUM) baselines. The pre-registration is archived separately on Zenodo (concept DOI 10.5281/zenodo.20188013). This preprint reports: (i) the registered protocol; (ii) a synthetic-pilot validation on a 16-series fractional Gaussian noise corpus that confirms the I/O accounting harness end-to-end and surfaces a methodological vulnerability in the primary outcome metric (bytes-read rewards aggressive subdivision on noise); (iii) Amendment 1 to the protocol, which adds a boundary-quality secondary outcome (F1 against ground-truth regime breaks on a new regime-switching synthetic corpus D4) without altering the primary outcome or the falsification rule. The empirical run on the registered datasets (S and Malempati Hari (2026), Multi-Scale Structural Descriptors for Governance-Relevant Patterns in Data Lineage Graphs (concept DOI 10.5281/zenodo.20099999).

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Hurst-Aware Adaptive Partitioning of Persistent Time Series: A Pre-Registered Protocol with Synthetic-Pilot Validation and a Boundary-Quality Methodology Amendment

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