Grammar of the Wave: Towards Explainable Multivariate Time Series Event Detection via Neuro-Symbolic VLM Agents
arXiv:2603.11479v1 Announce Type: new Abstract: Time Series Event Detection (TSED) has long been an important task with critical applications across many high-stakes domains. Unlike statistical anomalies, events are defined by semantics with complex internal structures, which are difficult to learn inductively from scarce labeled data in real-world settings. In light of this, we introduce Knowledge-Guided TSED, a new setting where a model is given a natural-language event description and must ground it to intervals in multivariate signals with little or no training data. To tackle this challenge, we introduce Event Logic Tree (ELT), a novel knowledge representation framework to bridge linguistic descriptions and physical time series data via modeling the intrinsic temporal-logic structures of events. Based on ELT, we present a neuro-symbolic VLM agent framework that iteratively instantiates primitives from signal visualizations and composes them under ELT constraints, producing both d
arXiv:2603.11479v1 Announce Type: new Abstract: Time Series Event Detection (TSED) has long been an important task with critical applications across many high-stakes domains. Unlike statistical anomalies, events are defined by semantics with complex internal structures, which are difficult to learn inductively from scarce labeled data in real-world settings. In light of this, we introduce Knowledge-Guided TSED, a new setting where a model is given a natural-language event description and must ground it to intervals in multivariate signals with little or no training data. To tackle this challenge, we introduce Event Logic Tree (ELT), a novel knowledge representation framework to bridge linguistic descriptions and physical time series data via modeling the intrinsic temporal-logic structures of events. Based on ELT, we present a neuro-symbolic VLM agent framework that iteratively instantiates primitives from signal visualizations and composes them under ELT constraints, producing both detected intervals and faithful explanations in the form of instantiated trees. To validate the effectiveness of our approach, we release a benchmark based on real-world time series data with expert knowledge and annotations. Experiments and human evaluation demonstrate the superiority of our method compared to supervised fine-tuning baselines and existing zero-shot time series reasoning frameworks based on LLMs/VLMs. We also show that ELT is critical in mitigating VLMs' inherent hallucination in matching signal morphology with event semantics.
Executive Summary
This article introduces a novel approach to Time Series Event Detection (TSED) called Knowledge-Guided TSED, which leverages a neuro-symbolic agent framework to bridge linguistic event descriptions with physical time series data. The Event Logic Tree (ELT) framework is a critical component of this approach, enabling the detection of events with complex internal structures in multivariate signals. The authors demonstrate the effectiveness of their method through experiments and human evaluation, showcasing its superiority over existing approaches. This work has significant implications for high-stakes domains and highlights the importance of knowledge-guided approaches in overcoming the limitations of deep learning models. The authors' innovative use of ELT and the neuro-symbolic agent framework provides a promising solution for TSED tasks, making this a valuable contribution to the field.
Key Points
- ▸ Introduction of Knowledge-Guided TSED, a new setting for Time Series Event Detection
- ▸ Development of Event Logic Tree (ELT) framework for bridging linguistic event descriptions and physical time series data
- ▸ Employment of a neuro-symbolic VLM agent framework for detecting events in multivariate signals
Merits
Strength in Handling Complex Events
The proposed approach effectively handles events with complex internal structures, addressing a significant limitation of existing methods.
Advancements in Explainability
The ELT framework provides a novel method for explaining event detections, offering insights into the underlying logical structures.
Improved Robustness
The knowledge-guided approach enhances the robustness of the model by leveraging domain knowledge to mitigate the hallucination inherent in VLMs.
Demerits
Scalability Challenges
The proposed method may face scalability issues when dealing with large-scale or high-dimensional time series data, which could impact its practical applicability.
Lack of Generalizability
The approach relies heavily on the quality and relevance of the provided domain knowledge, which may limit its generalizability to diverse domains and scenarios.
Computational Complexity
The ELT framework and the neuro-symbolic agent may introduce additional computational complexity, which could impact the model's efficiency and deployment in real-time applications.
Expert Commentary
The article presents a novel and innovative approach to Time Series Event Detection, addressing the significant challenge of handling complex events with internal structures. The development of the Event Logic Tree (ELT) framework and the employment of a neuro-symbolic VLM agent framework provide a promising solution for this task. However, as with any complex approach, there are scalability and generalizability challenges that need to be addressed. Nonetheless, the work showcases the importance of knowledge-guided approaches in overcoming the limitations of deep learning models, making it a valuable contribution to the field.
Recommendations
- ✓ Further investigation into the scalability and efficiency of the proposed approach is necessary to ensure its practical applicability in real-world scenarios.
- ✓ The authors should explore the potential of incorporating additional domain knowledge to enhance the robustness and generalizability of the model.