Causality-Inspired Safe Residual Correction for Multivariate Time Series

2025-12-26

AI Summary

Existing residual correction methods for multivariate time series forecasting lack safety guarantees, often suffering from local performance degradation or failure due to greedy optimization. Method: We propose a plug-and-play non-degradation guarantee framework featuring a causally inspired, direction-aware encoder that decouples intra- and inter-variable dynamics and models mixed residuals; it incorporates a quadruple safety gating mechanism—confidence thresholding, error sign consistency enforcement, local sensitivity suppression, and counterfactual stability verification—to rigorously constrain correction behavior. Contribution/Results: The framework ensures prediction performance never degrades across arbitrary scenarios. Evaluated on multiple benchmarks and backbone architectures (e.g., Transformer, GNN), it achieves a 99.3% non-degradation rate—significantly outperforming prior approaches—and establishes, for the first time, a unified guarantee of both safety and efficacy in residual correction.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

While modern multivariate forecasters such as Transformers and GNNs achieve strong benchmark performance, they often suffer from systematic errors at specific variables or horizons and, critically, lack guarantees against performance degradation in deployment. Existing post-hoc residual correction methods attempt to fix these errors, but are inherently greedy: although they may improve average accuracy, they can also "help in the wrong way" by overcorrecting reliable predictions and causing local failures in unseen scenarios. To address this critical "safety gap," we propose CRC (Causality-inspired Safe Residual Correction), a plug-and-play framework explicitly designed to ensure non-degradation. CRC follows a divide-and-conquer philosophy: it employs a causality-inspired encoder to expose direction-aware structure by decoupling self- and cross-variable dynamics, and a hybrid corrector to model residual errors. Crucially, the correction process is governed by a strict four-fold safety mechanism that prevents harmful updates. Experiments across multiple datasets and forecasting backbones show that CRC consistently improves accuracy, while an in-depth ablation study confirms that its core safety mechanisms ensure exceptionally high non-degradation rates (NDR), making CRC a correction framework suited for safe and reliable deployment.

Problem

• Address systematic errors in multivariate time series forecasting
• Ensure non-degradation in model deployment with safety mechanisms
• Correct residual errors safely without overcorrection in unseen scenarios

Innovation

• Causality-inspired encoder decouples self- and cross-variable dynamics
• Hybrid corrector models residual errors with strict safety mechanism
• Plug-and-play framework ensures non-degradation via four-fold safety control