MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models
arXiv:2602.13783v1 Announce Type: new Abstract: While Time Series Foundation Models (TSFMs) have demonstrated exceptional performance in generalized forecasting, their performance often degrades significantly when deployed in real-world vertical domains characterized by temporal distribution shifts and domain-specific periodic structures. Current solutions are primarily constrained by two paradigms: Domain-Adaptive Pretraining (DAPT), which improves short-term domain fitting but frequently disrupts previously learned global temporal patterns due to catastrophic forgetting; and Retrieval-Augmented Generation (RAG), which incorporates external knowledge but introduces substantial retrieval overhead. This creates a severe scalability bottleneck that fails to meet the high-efficiency requirements of real-time stream processing. To break this impasse, we propose Memory for Time Series (MEMTS), a lightweight and plug-and-play method for retrieval-free domain adaptation in time series foreca
arXiv:2602.13783v1 Announce Type: new Abstract: While Time Series Foundation Models (TSFMs) have demonstrated exceptional performance in generalized forecasting, their performance often degrades significantly when deployed in real-world vertical domains characterized by temporal distribution shifts and domain-specific periodic structures. Current solutions are primarily constrained by two paradigms: Domain-Adaptive Pretraining (DAPT), which improves short-term domain fitting but frequently disrupts previously learned global temporal patterns due to catastrophic forgetting; and Retrieval-Augmented Generation (RAG), which incorporates external knowledge but introduces substantial retrieval overhead. This creates a severe scalability bottleneck that fails to meet the high-efficiency requirements of real-time stream processing. To break this impasse, we propose Memory for Time Series (MEMTS), a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting. The key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics, such as recurring seasonal patterns and trends into a compact set of learnable latent prototypes. In doing so, it transforms fragmented historical observations into continuous, parameterized knowledge representations. This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency, while effectively mitigating catastrophic forgetting of general temporal patterns, all without requiring any architectural modifications to the frozen TSFM backbone. Extensive experiments on multiple datasets demonstrate the SOTA performance of MEMTS.
Executive Summary
The article introduces MEMTS, a novel approach for domain adaptation in Time Series Foundation Models (TSFMs) that addresses the challenges of catastrophic forgetting and retrieval overhead. MEMTS employs a Knowledge Persistence Module (KPM) to internalize domain-specific temporal dynamics into learnable latent prototypes, enabling efficient and accurate domain adaptation without modifying the TSFM backbone. The method achieves state-of-the-art performance in various datasets, demonstrating its effectiveness in real-world applications with high-efficiency requirements.
Key Points
- ▸ MEMTS addresses the limitations of Domain-Adaptive Pretraining (DAPT) and Retrieval-Augmented Generation (RAG) in TSFMs.
- ▸ The Knowledge Persistence Module (KPM) internalizes domain-specific temporal dynamics into compact, learnable latent prototypes.
- ▸ MEMTS achieves accurate domain adaptation with constant-time inference and near-zero latency, mitigating catastrophic forgetting.
- ▸ Extensive experiments demonstrate the state-of-the-art performance of MEMTS across multiple datasets.
Merits
Innovative Approach
MEMTS introduces a novel method for domain adaptation in TSFMs that effectively addresses the challenges of catastrophic forgetting and retrieval overhead, providing a significant advancement in the field.
Efficiency and Scalability
The method achieves constant-time inference and near-zero latency, making it highly efficient and scalable for real-time stream processing applications.
State-of-the-Art Performance
Extensive experiments demonstrate that MEMTS achieves state-of-the-art performance in domain adaptation tasks, validating its effectiveness.
Demerits
Limited Generalizability
While MEMTS shows promising results in specific datasets, its generalizability to other types of time series data and domains remains to be thoroughly explored.
Complexity of Implementation
The implementation of the Knowledge Persistence Module (KPM) may introduce complexity, requiring careful tuning and optimization for different applications.
Expert Commentary
The article presents a significant advancement in the field of domain adaptation for Time Series Foundation Models (TSFMs). The proposed MEMTS method effectively addresses the critical challenges of catastrophic forgetting and retrieval overhead, which have been major bottlenecks in the deployment of TSFMs in real-world applications. The innovative use of a Knowledge Persistence Module (KPM) to internalize domain-specific temporal dynamics into compact, learnable latent prototypes is a novel and promising approach. The method's ability to achieve constant-time inference and near-zero latency makes it highly efficient and scalable, addressing the high-efficiency requirements of real-time stream processing. Extensive experiments on multiple datasets demonstrate the state-of-the-art performance of MEMTS, validating its effectiveness. However, the generalizability of the method to other types of time series data and domains remains to be thoroughly explored. Additionally, the implementation of the KPM may introduce complexity, requiring careful tuning and optimization for different applications. Overall, the article makes a valuable contribution to the field and opens up new avenues for research and development in domain adaptation for TSFMs.
Recommendations
- ✓ Further research should be conducted to explore the generalizability of MEMTS to different types of time series data and domains.
- ✓ Future studies should focus on optimizing the implementation of the Knowledge Persistence Module (KPM) to reduce complexity and improve ease of use.
