{
  "abstract": "The Smooth Sign Accuracy (SSA) framework resolves the smoothness--accuracy dilemma for stationary predictors by balancing mean squared error (MSE) against the frequency of sign changes. We generalize it to non-stationary integrated processes (I-SSA), deriving MSE-optimal predictors constrained by the expected duration between turning points (I(1)) or inflection points (I(2)). The resulting maximal-monotone and minimal-inflection predictors, filters, and smoothers customize classic benchmarks along a new efficient Accuracy--Smoothness (AS) frontier. Tracking the identity (the original data) along this frontier defines the \\emph{I-SSA trend}, a novel smoothing concept that, unlike classical filters, imposes no artificial structure on the data-generating process and is directly interpretable through explicit control of turning- or inflection-point frequency. The optimization is a quadratically constrained quadratic program (QCQP) that reduces to a Generalized Trust Region Subproblem (GTRS) with hidden convexity. On its feasible, invertible region the problem is convex with a well-defined global optimum, and an exact closed-form expression delivers the efficient AS frontier. Under fairly general assumptions, finite-sample solutions converge to an asymptotic I-SSA predictor, devoid of integration boundary perturbations. The framework unifies mean squared error on non-stationary levels, and sign accuracy, zero-crossing frequency, and target correlation on stationary differences.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41963",
  "author_total": 1,
  "authors": [
    {
      "name": "Marc Wildi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/erd5z8bcmzxsd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41963",
  "fulltext_url": null,
  "id": "erd5z8bcmzxsd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41963",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41963",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/erd5z8bcmzxsd.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:25Z",
      "native_id": "2610.41963",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41963"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Zero Crossings on the Accuracy-Smoothness Frontier: Maximal Monotone and Minimal Inflection Non Stationary Predictors",
  "title_text": "Zero Crossings on the Accuracy-Smoothness Frontier: Maximal Monotone and Minimal Inflection Non Stationary Predictors",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/erd5z8bcmzxsd",
  "venue": "arXiv",
  "versions": []
}