A Reproducible Statistical-Learning Framework for Sparse Time-Indexed Transaction Data
- 1Faculty of Economic Sciences, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania
- 2Independent Researcher, 550324 Sibiu, Romania
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.
Related
- A Hybrid Spatial Statistical Learning Framework for Individualized Probability Estimation: Application to Multisite Autism Neuroimaging
- A Latent-Space Statistical Learning Framework for Semicontinuous Outcomes: Regularized Deviance and Boundary-Stabilized Optimization
- Context-Guided Traversal with Deep Reinforcement Learning for Suspicious Subgraph Detection in Cryptocurrency Transaction Graphs
- High-Dimensional Learning Dynamics of Attention-Indexed Models
- Nonparametric In-Context Learning and Inference via Measure-Indexed Kernel Ridge Regression
BibTeX
@article{cristescu2026reproducible,
title = {{A Reproducible Statistical-Learning Framework for Sparse Time-Indexed Transaction Data}},
author = {Marian Pompiliu Cristescu and Ioana Petrea},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14193598},
url = {https://www.mdpi.com/2227-7390/14/19/3598},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Reproducible Statistical-Learning Framework for Sparse Time-Indexed Transaction Data
AU - Marian Pompiliu Cristescu
AU - Ioana Petrea
PY - 2026
DA - 2026/10/03
JO - Mathematics
DO - 10.3390/math14193598
UR - https://www.mdpi.com/2227-7390/14/19/3598
AB - 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.
SN - 2227-7390
ER -