{
  "abstract": "Sparse time-indexed transaction data create a statistical-learning problem in which low basket occupancy, combinatorial candidate growth, temporal heterogeneity, and data-quality sensitivity complicate interpretable pattern discovery. Using a multi-year single-case dataset from an independently operated SME restaurant point-of-sale (POS) system, this study proposes a reproducible, sparsity-aware framework that separates global distributional characterization, unsupervised co-occurrence learning, statistical qualification, adjusted outcome modeling, and transparent prioritization. The empirical application comprises 31,157 baskets, 139,396 valid item lines, 710 products, and 762 active days; the 31,157 × 710 basket-product representation has 0.630% density. Multiplicity-screened association-rule learning retains 1174 directional rules. In a strictly subsequent validation period, 93.6% of discovery rules preserve lift above one, 49.3% satisfy all prespecified thresholds again, and rank correlations for support, confidence, and lift range from 0.803 to 0.849. An estimated-dispersion NB2 model with day-clustered uncertainty preserves the principal basket-breadth associations, while Gamma sensitivity analysis confirms the direction, but not invariant magnitude, of late-service basket-value effects. The methodological contribution is not a new stand-alone algorithm, but an auditable evidence architecture that explicitly separates candidate discovery, multiplicity screening, effect assessment, temporal replication, adjusted modeling, and human-supervised prioritization. Because the empirical evaluation is based on a single time-indexed case, the reported effect magnitudes remain case-specific.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Economic Sciences, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania",
    "Independent Researcher, 550324 Sibiu, Romania"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Marian Pompiliu Cristescu",
      "orcid": "0000-0003-3638-4379"
    },
    {
      "affs": [
        1
      ],
      "name": "Ioana Petrea"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5wdrgvee8nfty.bib",
  "cited_by": 0,
  "doi": "10.3390/math14193598",
  "fulltext_url": null,
  "id": "5wdrgvee8nfty",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/19/3598",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1710|Data Mining Algorithms and Applications"
    ]
  ],
  "openalex": "W7211899231",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/5wdrgvee8nfty.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14193598",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/19/3598"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "A Reproducible Statistical-Learning Framework for Sparse Time-Indexed Transaction Data",
  "title_text": "A Reproducible Statistical-Learning Framework for Sparse Time-Indexed Transaction Data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/5wdrgvee8nfty",
  "venue": "Mathematics",
  "versions": []
}