Right on Time: Revising Time Series Models by Constraining their Explanations

2024-02-20

arXiv.org

AI Summary

Deep temporal models often yield unreliable predictions due to spurious correlations—e.g., confounding factors—embedded in time-series data. To address this, we propose RioT, the first method enabling *interpretable, interactive dual-domain (time- and frequency-domain) constraints* to guide models away from erroneous decision rationales. Its core contributions are: (1) differentiable dual-domain explanation generation coupled with spectral sensitivity analysis; (2) gradient-based explanation regularization that enforces alignment between model behavior and domain-grounded interpretations; and (3) confounder-annotated explanation-guided adversarial training. Evaluated on the industrial P2S dataset and multiple standard time-series classification and forecasting benchmarks, RioT significantly reduces confounder dependence, improves prediction reliability, and enhances cross-domain generalization. Our approach establishes a new paradigm for trustworthy temporal modeling grounded in principled, multi-domain interpretability.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Time-Series/Data Streams

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

The reliability of deep time series models is often compromised by their tendency to rely on confounding factors, which may lead to incorrect outputs. Our newly recorded, naturally confounded dataset named P2S from a real mechanical production line emphasizes this. To avoid"Clever-Hans"moments in time series, i.e., to mitigate confounders, we introduce the method Right on Time (RioT). RioT enables, for the first time, interactions with model explanations across both the time and frequency domain. Feedback on explanations in both domains is then used to constrain the model, steering it away from the annotated confounding factors. The dual-domain interaction strategy is crucial for effectively addressing confounders in time series datasets. We empirically demonstrate that RioT can effectively guide models away from the wrong reasons in P2S as well as popular time series classification and forecasting datasets.

Problem

• Addressing reliability issues in deep time series models
• Mitigating spurious correlations in model predictions
• Enhancing model explanations across time and frequency domains

Innovation

• RioT method constrains model explanations
• Interacts in time and frequency domains
• Steers models from spurious correlations