Introducing TAM: A PyTorch-Based Additive Forecasting Framework and its Application to Explainable Anomaly Detection

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Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-06
DOI
https://doi.org/10.5281/zenodo.22550393
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Introducing TAM: A PyTorch-Based Additive Forecasting Framework and its Application to Explainable Anomaly Detection

Yann Allioux
Zenodo (CERN European Organization for Nuclear Research)
Anomaly Detection Techniques and Applications
article

Introducing TAM: A PyTorch-Based Additive Forecasting Framework and its Application to Explainable Anomaly Detection

Yann Allioux
article en

Abstract

This is the official slide deck for the presentation "Introducing TAM: A PyTorch-Based Additive Forecasting Framework and its Application to Explainable Anomaly Detection," delivered at the DASS Workshop (Anomaly detection for modern data) on September 8th, 2026. Overview: The presentation introduces the Time series Additive Model (TAM) v1.3.0, a PyTorch-based framework that combines the transparency of standard statistical models with exact, GPU-scaled primal resolution. It demonstrates how an explainable additive model can not merely flag an anomaly, but adapt to it through both time and space. The framework's capabilities are illustrated through two distinct case studies: Power Grids (Time): Forecasting French national electricity load through severe structural regime shifts, specifically the COVID-19 lockdowns and the 2022 energy crisis. It highlights the use of sliding-window correctors (AdaptiveTAM) and dynamic expert aggregation (OperaTAM) to track shifting mechanisms in real-time. Real Estate (Space): Detecting and mathematically deconstructing spatial price anomalies in the French housing market. It showcases TAM's new statistical layer, utilizing non-Gaussian distributional location-scale solves combined with Conformal Quantile Regression (CQR) to generate exact, explainable price intervals. Related Resources: Workshop Code & Reproducibility: https://github.com/yallioux/2026_dass_tam_for_anomalies TAM Software Framework: https://doi.org/10.5281/zenodo.22544995 FORCE Dataset: https://doi.org/10.5281/zenodo.21109134

Zenodo (CERN European Organization for Nuclear Research)
Électricité de France (France) (FR)
Openalex Percentile: Top 8%
Anomaly Detection Techniques and Applications
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