Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interaction
Shiyan Hu, Jianxin Jin, Yang Shu, Peng Chen, Bin Yang, Chenjuan Guo
TL;DR
This paper introduces MindTS, a novel multimodal time series anomaly detection model that aligns heterogeneous data semantically and filters redundant information to improve detection accuracy across multiple real-world datasets.
Contribution
The paper presents a new approach combining semantic alignment and content condensation for multimodal anomaly detection, addressing key challenges in cross-modal interaction.
Findings
MindTS achieves superior anomaly detection performance on six datasets.
Semantic alignment improves cross-modal consistency.
Content condensation enhances interaction by filtering redundant information.
Abstract
Time series anomaly detection plays a critical role in many dynamic systems. Despite its importance, previous approaches have primarily relied on unimodal numerical data, overlooking the importance of complementary information from other modalities. In this paper, we propose a novel multimodal time series anomaly detection model (MindTS) that focuses on addressing two key challenges: (1) how to achieve semantically consistent alignment across heterogeneous multimodal data, and (2) how to filter out redundant modality information to enhance cross-modal interaction effectively. To address the first challenge, we propose Fine-grained Time-text Semantic Alignment. It integrates exogenous and endogenous text information through cross-view text fusion and a multimodal alignment mechanism, achieving semantically consistent alignment between time and text modalities. For the second challenge,…
Peer Reviews
Decision·ICLR 2026 Poster
1. Addresses a novel and underexplored problem: multimodal time series anomaly detection. 2. Introduces a well-motivated novel dual-text approach for semantic alignment. 3. Extensive experiments with multiple datasets and a strong set of 17+ baselines. 4. Comprehensive ablation and sensitivity analyses that validate the contribution of each component.
1. Dependence on the quality and availability of exogenous text may reduce generalizability to domains lacking rich textual context. 2. The discussion could benefit from more qualitative examples showing how textual alignment improves anomaly detection decisions. 3. Computational cost and inference time are not reported.
1. The paper proposes a well-designed dual-view text alignment strategy that fuses endogenous and exogenous text to achieve precise semantic alignment between time and text modalities, effectively addressing heterogeneous data alignment issues. 2. The proposed Content Condenser, inspired by the Information Bottleneck principle, provides a principled solution to mitigate textual redundancy and noise in multimodal fusion, improving the efficiency of cross-modal interaction. 3. The cross-modal re
1. The paper claims to be multimodal, mentioning images and videos, but the proposed MindTS framework is actually limited to time-series–text fusion. Its core mechanisms are difficult to extend to other modalities. The authors should clarify this scope in the paper. 2. The paper lacks sufficient ablation on the design of endogenous prompts. Since the construction of these prompts (e.g., template formulation, choice of statistical descriptors, and temporal granularity) may strongly affect model p
1. The paper identifies multi-scale semantic discrepancy as a key blind spot in existing LLM-for-time-series (LLM4TS) approaches. The proposed hyperedge mechanism effectively models group-level interactions through learnable hyperedges, which I think could reduce noise compared to traditional patch-based methods such as PatchTST. 2. The experimental validation appears thorough and convincing. 3. The framework demonstrates compatibility with advanced LLMs such as LLaMA-3.1-8B and Qwen2.5-7B, whic
1. The hyperedging mechanism lacks interpretability. Although experiments show it improves semantic density, it remains unclear how specific hyperedges correspond to concrete multi-scale temporal patterns (e.g., daily vs. weekly cycles). The choice of the Top-K threshold (η = 4) also seems purely empirical, with limited discussion on why this value is optimal or how sensitive performance is to η. 2. The necessity of some MoP components is uncertain. The emotional manipulation prompts (e.g., “Tha
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Time Series Analysis and Forecasting · Topic Modeling