NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates
NVExplain is a model-agnostic explainability framework for time series forecasting that attributes each forecast horizon to temporally relevant historical lags The framework introduces "semantic flow" to quantify how information evolves across time within the model's internal representations, modeling forecasting as a latent trajectory A lag-horizon attribution matrix is constructed by aggregating semantic flow, capturing horizon-resolved temporal influence that existing methods miss Structure-p
Analysis
TL;DR
- NVExplain is a model-agnostic explainability framework for time series forecasting that attributes each forecast horizon to temporally relevant historical lags
- The framework introduces "semantic flow" to quantify how information evolves across time within the model's internal representations, modeling forecasting as a latent trajectory
- A lag-horizon attribution matrix is constructed by aggregating semantic flow, capturing horizon-resolved temporal influence that existing methods miss
- Structure-preserving perturbations and sparse local surrogate models are used to produce human-readable, temporally coherent explanations
- The semantic-flow variant achieves competitive or superior faithfulness compared to standard post-hoc baselines while being substantially more computationally efficient, with stability analysis identifying caution regimes
Why It Matters
Time series forecasting is increasingly deployed in high-stakes domains such as healthcare, finance, and energy, yet interpretability has lagged behind predictive performance. NVExplain addresses a critical gap by providing horizon-specific explanations that respect temporal dependence, enabling practitioners to trust and audit model decisions in ways previous post-hoc methods could not. This is particularly relevant as regulatory and operational demands for explainable AI continue to grow across industries relying on sequential predictions.
Technical Details
- Latent trajectory modeling: The framework treats the forecasting process as a latent trajectory through the model's internal representations, rather than treating each horizon independently, preserving the temporal structure inherent in sequential data.
- Semantic flow quantification: A novel concept that measures how information propagates and transforms across time steps within the model, aggregated into a lag-horizon attribution matrix that maps each historical lag to each forecast horizon with temporal resolution.
- Structure-preserving perturbations: Instead of random or naive perturbations that destroy temporal dependencies, NVExplain generates perturbations that maintain the structural properties of time series data, ensuring that surrogate model fitting operates on realistic inputs.
- Sparse local surrogate models: After perturbation, sparse linear surrogates are fitted locally around each prediction, producing human-readable explanations that are temporally coherent and directly interpretable by domain experts.
- Evaluation: Faithfulness and stability diagnostics were conducted across multiple benchmark datasets, demonstrating that the semantic-flow variant matches or exceeds standard post-hoc baselines in faithfulness while achieving substantially lower computational cost.
Industry Insight
- Organizations deploying time series models in production should prioritize explainability frameworks that respect temporal structure rather than applying generic tabular XAI methods, which can produce misleading or temporally incoherent attributions.
- The computational efficiency of NVExplain makes it viable for real-time or near-real-time explanation pipelines, a key requirement for monitoring and auditing forecasting systems in dynamic environments like trading or grid management.
- The stability analysis identifying "caution regimes" provides a practical diagnostic tool: practitioners can use it to flag predictions where explanations are unreliable, enabling risk-aware deployment decisions and targeted model retraining.
Disclaimer: The above content is generated by AI and is for reference only.