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Trustworthy electricity theft detection integrating informative missingness learning and certified conformal audits

This repository contains the official open-source verification framework, neural architecture implementations, and experiment replication suite for the research paper: "Trustworthy electricity theft detection integrating informative missingness learning and certified conformal audits" (Under review at Scientific Reports, Nature Portfolio). Key Components: 1. Missingness-Aware Multi-Representation Detector (MAMRD): A compact 0.97M-parameter deep learning architecture integrating selective state-space models (Mamba), zero-sum Laplacian differencing, and context-conditional missingness embeddings. 2. Leakage-Free Native-Prevalence Protocol: Stan

By Akram, Hafiz Muhammad Azeem, Talatahari, Siamak, Jhangeer, AdilToolReference information

Resource information

Format
zip
Original repository
23727783
DOI
10.5281/zenodo.23727783
Credit basis
Creators in Zenodo record metadata
Access
Open files available from the original Zenodo deposit
Last checked
Nov 5, 2026
License
cc-by-4.0
Last record update
Nov 5, 2026

About this resource

This repository contains the official open-source verification framework, neural architecture implementations, and experiment replication suite for the research paper: "Trustworthy electricity theft detection integrating informative missingness learning and certified conformal audits" (Under review at Scientific Reports, Nature Portfolio). Key Components: 1. Missingness-Aware Multi-Representation Detector (MAMRD): A compact 0.97M-parameter deep learning architecture integrating selective state-space models (Mamba), zero-sum Laplacian differencing, and context-conditional missingness embeddings. 2. Leakage-Free Native-Prevalence Protocol: Standardized evaluation pipelines for the State Grid Corporation of China (SGCC) and ElectricityLoadDiagrams (ECL) benchmarks preventing temporal and customer-level data leakage. 3. Certified Conformal Audits: Post-hoc class-conditional conformal prediction routines certifying finite-sample theft recovery guarantees across heterogeneous consumption tiers. 4. Baselines: Production implementations of rival architectures (GRU-D, WDCNN, HORLN, Self-Attention, and MSDM).