arXiv:2412.19897v3 Announce Type: replace-cross Abstract: We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series 'base model' is used. 'Explainability' of the correction is provided by fitting the base model again to the data from which the error prediction is removed (subtracted), yielding a difference in the model parameters which can be interpreted. We provide illustrative examples to demonstrate the potential of the method to discover and explain underlying patterns
The increasing complexity of AI models necessitates methods for understanding their decision-making processes, especially in critical applications like time series forecasting.
Improving the explainability of complex predictive models builds trust and enables better human oversight and intervention, crucial for broader AI adoption in sensitive domains.
The ability to understand and interpret model corrections provides a pathway to more reliable and auditable AI-driven forecasting systems, mitigating 'black box' risks.
- · AI explainability researchers
- · Industries relying on time series forecasting (e.g., finance, energy)
- · Regulatory bodies developing AI governance frameworks
- · Developers of entirely opaque AI models
- · Organizations unwilling to invest in model interpretability
Improved reliability and acceptance of AI-driven forecasts across various sectors.
Reduced regulatory hurdles for deploying advanced AI in highly regulated environments.
Accelerated development of generalizable explainability frameworks for diverse AI architectures, potentially aiding broader AI agent development.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG