Skip to content
DraconiticResearch
Search Sources API
194,164 studies31 sourcesUpdated 53 min ago
PreprintarXiv licencearXiv

GRACE: A General, Exact Changepoint Calculus

Abstract

We introduce GRACE (the General Regime-Aware Changepoint Estimator), a general, globally exact multiple-changepoint detection and estimation framework in which arbitrary numbers of model parameters may change at or persist across estimated changepoint locations, and all permissible changepoint types and patterns of parameter sharing compete within a single ℓ0-penalized optimization. The analyst specifies which types of changepoints are permitted and which parameters each type must preserve, without specifying which changes actually occur, where they occur, or in what order. GRACE jointly estimates the number and locations of changepoints, the type of change at each location, the model fitted between changepoints, and all continuous parameters. As a direct consequence, GRACE can estimate models in which the slope persists across a mean shift. The generality of the framework is achieved through an exact dynamic-programming recursion that retains the accumulated fitting cost conditional on whichever parameters a subsequent change may preserve, tracking multiple parameters jointly when necessary. We establish the global exactness of the recursion and develop functional, penalty-based, and objective-bound pruning rules that eliminate candidates without sacrificing optimality. Ordinary optimal partitioning, FPOP, and CPOP are recovered as special cases, while piecewise-polynomial and seasonal models illustrate the broader range of permissible segment structures and transitions. The resulting framework supports heterogeneous changepoint types, parameter sharing across segments, and substantial parallel computation within a unified approach to exact changepoint estimation.

Related

  1. A general framework for universal bounds on the analytic functional calculus
    Mihai Putinar, Georgios Tsikalas
    arXivPreprintCC BY 4.0Full textAnalysis & PDEsarXiv
  2. voigtinference: Exact likelihood calculus and conditional attribution for the Voigt profile
    Peter Reinhard Hansen, Chen Tong
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  3. Rough paths below the Young threshold: an exact scale calculus and the locality phase transition at one quarter
    Zongjian Han
    arXivPreprintarXiv licenceFull textAnalysis & PDEsProbabilityarXiv
  4. Exact SU(N) symmetry resolution of bosonic Hubbard models: general boson-fermion mappings and flat-band singlet-cluster states
    Loïc Herviou
    HALPreprintCC BY-NC 4.0Full textMathematical physicsAtomic, molecular & optical physicsHALarXiv
  5. Generalized Linear Mixed Models for Changepoint Analysis of Biomedical Time Series Data
    Mark Fiecas, Rebecca Killick, Kathryn Cullen
    Statistics in medicinePeer-reviewedCC BYFull textStatisticsEurope PMC
BibTeX
@misc{grantham2026grace,
  title = {{GRACE: A General, Exact Changepoint Calculus}},
  author = {Michael Grantham},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2609.28831},
  eprint = {2609.28831},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.28831},
}
RIS
TY  - UNPB
TI  - GRACE: A General, Exact Changepoint Calculus
AU  - Michael Grantham
PY  - 2026
DA  - 2026/10/12
PB  - arXiv
DO  - 10.48550/arxiv.2609.28831
UR  - https://arxiv.org/abs/2609.28831
AB  - We introduce GRACE (the General Regime-Aware Changepoint Estimator), a general, globally exact multiple-changepoint detection and estimation framework in which arbitrary numbers of model parameters may change at or persist across estimated changepoint locations, and all permissible changepoint types and patterns of parameter sharing compete within a single ℓ_0-penalized optimization. The analyst specifies which types of changepoints are permitted and which parameters each type must preserve, without specifying which changes actually occur, where they occur, or in what order. GRACE jointly estimates the number and locations of changepoints, the type of change at each location, the model fitted between changepoints, and all continuous parameters. As a direct consequence, GRACE can estimate models in which the slope persists across a mean shift. The generality of the framework is achieved through an exact dynamic-programming recursion that retains the accumulated fitting cost conditional on whichever parameters a subsequent change may preserve, tracking multiple parameters jointly when necessary. We establish the global exactness of the recursion and develop functional, penalty-based, and objective-bound pruning rules that eliminate candidates without sacrificing optimality. Ordinary optimal partitioning, FPOP, and CPOP are recovered as special cases, while piecewise-polynomial and seasonal models illustrate the broader range of permissible segment structures and transitions. The resulting framework supports heterogeneous changepoint types, parameter sharing across segments, and substantial parallel computation within a unified approach to exact changepoint estimation.
ER  -