MERIT : Multi-Agent Collaboration for Unsupervised Time Series Representation Learning (2025)

Abstract: This paper studies the problem of unsupervised time series representation learning, which aims to map unlabeled time series data into a low-dimensional latent space for various downstream tasks. Previous works usually combine a range of augmentation strategies with contrastive learning to generate discriminative representations. However, these augmentation strategies could alter the original semantics of time series data, which could degrade the performance of representation learning. To solve this problem, this paper incorporates the large language model (LLM) agent to guide unsupervised time series representation learning and proposes a novel framework named Multi-Agent Collaboration for Time-series Representation Learning (MERIT). The core of our MERIT is to utilize three LLM agents to collaboratively generate positive views for time series data. In particular, we first design a retrieval agent to automatically identify the relevant time series data from a coarse candidate set. Then, these selected sequences are further utilized to enhance an augmentation agent which automatically selects reliable augmentation strategies from an augmentation strategy library. We also design a review agent to evaluate the quality of generated views and stop the generation process. These three agents are designed to work in a loop for effective time series representation learning. Extensive experiments on multiple time series datasets demonstrate the effectiveness of our MERIT in comparison with state-of-the-art baselines.

## introduction in the data-driven era, time series data exist widely in various fields including finance @xcite , healthcare @xcite , and transportation (sun * these authors contributed equally to this work. due to the strong capacity of large language models (llms) @xcite @xcite , we aim to incorporate llms into time series representation learning. however, there are still two major challenges in applying llms to time series data representation learning. ❶ llms usually have a limited understanding of numerical signals @xcite @xcite . therefore, it is hard to require llms to directly generate reliable time series representations or augmented time series. llms could even generate wrong formats with the principle of next-token predictions. ❷ their ability for semantic understanding and feature extraction is insufficient under the zero-shot condition @xcite @xcite , especially for complicated time series. therefore, we should introduce contextual search and welldesigned instructions to lay a solid foundation for constructing a more flexible, robust, and semantically preserved unsupervised time series representation learning framework. towards this end, we propose a novel framework named multi-agent collaboration for timeseries representation learning (merit). different from existing representation learning methods, our merit utilizes three llm-based agents to automatically generate high-quality positive sample views for time series data (see figure 1 ), and thus achieve more reliable representations. in particular, we first introduce a retrieval agent, which utilizes the semantic understanding and association inference ability to select the relevant sequences from the database. to enhance the retrieval efficiency, we also calculate the similarity of time series which generate a coarse candidate. these selected relevant sequences are considered as context data with instructions for an augmentation agent, which automatically identifies reliable augmentation schemes from an augmentation strategy library. in this way, we can generate positive views that match the data characteristics, ensuring that the critical semantics can be preserved as much as possible for effective representation learning. to further ensure the reliability without potential hallucination, we introduce a review agent to evaluate the quality of the generated augmented views and terminate unsuitable augmentations if necessary. finally, both appropriate augmented views and retrieved sequences are incorporated into a contrastive time series representation learning paradigm. extensive experiments on a wide range of benchmark datasets validate the effectiveness of our merit in comparison with various state-of-the-art approaches. the contributions of this paper are as follows: ❶ problem connection. we are the first to connect llm agents with time series representation learning, which utilizes the reasoning capability of llms to enhance the time series learning paradigm. ❷ novel methodology. our multi-agent collaborate framework merit utilizes a retrieval agent to extract context from the data, which enhances the reasoning ability of an augmentation agent for effective time series positive views. a review agent is also adopted to ensure the reliability. ❸ extensive experiments. extensive experiments on several publicly available time series datasets results show that merit significantly outperforms existing comparative learning methods. ## the proposed merit this paper studies the problem of time series representation learning and proposes a multi-agent framework named merit, which incorporates three llm-based intelligent agents in a closedloop collaboration mechanism to dynamically generate high-quality positive views while ensuring the preservation of semantics. in particular, our framework consists of three llm-based agents: ## experiments baselines. to evaluate the performance of merit, we compare it with a wide range of state-of-the-art baselines, including unsupervised, self-supervised, and fully supervised methods: dtw @xcite , donut @xcite , sr @xcite , n-beats (oreshkin et al., 2019), logtrans @xcite , ts-tcc @xcite , tnc @xcite , t-loss @xcite , informer @xcite , tst @xcite , cost @xcite , ts2vec @xcite , infots @xcite and timesurl @xcite ). these methods are evaluated across various downstream tasks, including classification, imputation, forecasting, anomaly detection, and transfer learning. more details can be found in appendix c. implementation details. for merit, we implement a multi-agent collaboration framework consisting of three specialized agents. each agent is designed with specific roles and responsibilities in the representation learning process. more details can be found in the appendix e. ## conclusion in this paper, we study the problem of unsupervised time series representation learning and we present a novel multi-agent collaboration framework merit for this problem. by leveraging llms as intelligent decision-makers, our framework introduces three agents with different functionalities that work collaboratively to generate high-quality positive views for contrastive learning. extensive experiments demonstrate that merit consistently outperforms existing methods across various downstream tasks. future research directions include optimizing llm inference efficiency, and extending the framework to handle multi-modal time series data.