Title: Many Minds, One Goal: Time Series Forecasting via Sub-task Specialization and Inter-agent Cooperation
Venue: NeurIPS 2025
Accepted: yes
Keywords: time series forecasting
Abstract: Time series forecasting is a critical and complex task, characterized by diverse temporal patterns, varying statistical properties, and different prediction horizons across datasets and domains. Conventional approaches typically rely on a single, unified model architecture to handle all forecasting scenarios. However, such monolithic models struggle to generalize across dynamically evolving time series with shifting patterns. In reality, different types of time series may require distinct modeling strategies. Some benefit from homogeneous multi-scale forecasting awareness, while others rely on more complex and heterogeneous signal perception. Relying on a single model to capture all temporal diversity and structural variations leads to limited  performance and poor interpretability. To address this challenge, we propose a Multi-Agent Forecasting System (MAFS) that abandons the one-size-fits-all paradigm. MAFS decomposes the forecasting task into multiple sub-tasks, each handled by a dedicated agent trained on specific temporal perspectives (e.g., different forecasting resolutions or signal characteristics).  Furthermore, to achieve holistic forecasting, agents share and refine information through different communication topology, enabling cooperative reasoning across different temporal views. A lightweight voting aggregator then integrates their outputs into consistent final predictions. Extensive experiments across 11 benchmarks demonstrate that MAFS significantly outperforms traditional single-model approaches, yielding more robust and adaptable forecasts.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel and effective framework, the Multi-Agent Forecasting System (MAFS), to address the inherent diversity and complexity of time series forecasting tasks. Unlike traditional single-model approaches, MAFS decomposes the problem into multiple specialized agents, each focusing on different temporal patterns or signal characteristics. These agents collaborate through flexible communication topologies and their outputs are aggregated via a lightweight voting mechanism. The approach is well-motivated, intuitive, and supported by extensive experiments across 11 benchmark datasets, demonstrating its superior robustness and adaptability compared to conventional forecasting models.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\nS1. Exploring a multi-agent system for time-series forecasting tasks is a novel and meaningful attempt, as forecasting inherently involves multiple factors and randomness. The proposed MAFS framework with multi-role agents improves the robustness of prediction process.\n\nS2. MAFS decomposes the forecasting task into trend, seasonality, frequency, and distribution agents, which intuitively makes sense and enhances interpretability.\n\nS3. The experimental results are comprehensive, which validate the consistent effectiveness of MAFS across 11 real-world datasets, and further verifies the robustness and adaptiveness of the multi-agent system across various datasets. The ablation studies confirm that the multi-agent system outperforms single-model baselines.\n\nS4. The paper is well-structured, with clear figures and strong readability.\n\n**Weaknesses:**\n\nW1. MAFS performs worse than Crossformer on the AQShunyi and AQWan datasets, and the authors have not provided a detailed analysis of the reasons behind this.\n\nW2. There are two typos in the paper: line 270 \"MAS forecasting system\" and line 194 \"H^{L} \\_i\", which should be corrected. In addition, the decimal places in Table 1 should be consistently kept to three digits.\n\nW3. In Section 5.7 (Case Study), the learned interaction weights among agents are visualized, but it appears that self-interaction weights are consistently dominant. The paper lacks an analysis of this phenomenon.\n\nQUESTIONS:\nQ1. Why was iTransformer chosen as the backbone for the agents in MAFS? Have other backbone networks been tried?\n\nQ2. The communication structures between agents are limited to graph/star/ring/chain. What is the rationale behind this selection? Are there other possible communication topologies worth exploring?\n\nQ3. For the distribution-focused agent, why was the design to predict six separate statistical values instead of directly modeling the future data distribution?\n\nLIMITATIONS:\nYes, the authors have properly discussed the potential limitations of this research.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel Multi-Agent Forecasting System (MAFS) for time series forecasting, which abandons the traditional one-size-fits-all model paradigm. MAFS decomposes the forecasting task into multiple sub-tasks, each handled by a dedicated agent trained on specific temporal perspectives, such as different forecasting resolutions or signal characteristics. These agents then share and refine information through structured communication topologies, enabling cooperative reasoning across different temporal views. A lightweight voting aggregator integrates their outputs into consistent final predictions.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n1. The authors propose principled task decomposition strategies, including multi-scale temporal forecasting and multi-aspect signal forecasting. These strategies enable agents to specialize in distinct aspects of the time series, capturing diverse temporal dynamics and improving overall accuracy.\n2. The paper introduces an explicit communication mechanism among agents, implemented via graph convolutional networks. This allows agents to share contextual information, enhancing their generalization capacity and overcoming the limitations of isolated modeling.\n3. MAFS is designed to be scalable and flexible, allowing for different communication topologies (e.g., ring, star, chain, fully-connected) and varying numbers of agents. This flexibility enables the system to adapt to different forecasting scenarios and datasets.\nWeaknesses\n1. The paper mentions occasional instability during training, especially when the number of agents increases or when dealing with highly volatile datasets.\n2. The multi-agent framework introduces additional complexity and computational overhead compared to single-model approaches. While the authors demonstrate scalability, the increased computational requirements may be a barrier for real-time applications or resource-constrained environments.\n3. The performance of MAFS is sensitive to the choice of communication topology. While the star topology is shown to be effective, selecting the optimal topology for a given dataset or task may require additional tuning and experimentation.\n4. The paper does not provide a comprehensive theoretical analysis of the proposed framework, such as convergence guarantees or bounds on forecasting accuracy. This lack of theoretical grounding may make it difficult to fully understand the system's behavior and potential limitations.\n5. With multiple specialized agents, there is a risk of overfitting to the training data, especially if the agents focus on capturing noise rather than underlying patterns.\n\nQUESTIONS:\n1. Why multiple agents are needed for time series forecasting problem? Is it making the problem more complex in an unnecessary way, when many time series forecasting problems are domain-specific and can be well resolved with a single deep learning model?\n2. What about the benefit-cost tradeoff? While some improvements in the forecasting accuracy is achieved, what are the computational cost increment?\n3. While transfer learning can be well used for knowledge sharing among different time series datasets, why no consider this approach?\n4. How to valid the robustness of the proposed approach, e.g., when the number of unseen datasets increase?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a multi-agent forecasting system (MAFS) that tackles shortcomings of single-model forecasters, particularly under non-stationary conditions. MAFS splits the overall task into several subtasks such as different segments of the prediction horizon so each agent can specialize. Agents share information through structured message-passing graph, and their outputs are combined by a global voter weighted by per-agent confidence scores. With iTransformer serving as the backbone encoder, MAFS outperforms a range of baseline models.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:** \n- S1 The authors clearly articulate three key challenges in multi-agent forecasting—task decomposition, limited perception, and collaboration bottlenecks—and address each one.\n    \n- S2 The framework is modular and extensible: agents are plug-and-play encoders, and the communication graph is topology-agnostic, so the system should adapt to future backbones or larger agent pools.\n    \n- S3 The experiments cover multiple data sets, communication topologies, agent scales, and task-division schemes.\n    \n- S4 Figure 1 is especially helpful and well designed; overall, the figures are clean and easy to follow.\n\n\n\n**Weaknesses:** \n- W1 At a high level (this is very simplified) MAFS resembles an ensemble. The paper positions itself against Mixture-of-Experts but stops short of an empirical comparison with a strong MoE baseline or standard bagging/stacking ensembles.\n- W2 Empirical scope feels narrow (and a bit unfair): The technical story checks out, but in practice the authors only ever wrap one encoder—iTransformer—inside MAFS. That leaves me wondering whether the gains come from the multi-agent trick or just from picking a lucky backbone. If, as you claim, _any_ encoder can slot in, then please show it: TimeMixer + MAFS, PatchTST + MAFS, maybe even an old RNN + MAFS. Stack those side-by-side with their vanilla counterparts the same way iTransformer is treated in Table 1. Seeing that full matrix would make the numbers feel a lot more convincing (and, frankly, would shut down the \"it’s just an ensemble\" objection). Right now the comparison feels lopsided, and it’s hard to judge how much credit belongs to the framework itself.\n- W3 Currently, recent SOTA are sparsely covered. Models such as CycleNet, xLSTM-Mixer, (_not as encoder_ MoEs) and State Space Models (e.g., S-Mamba, Chimera) are absent from Table 1, leaving open whether MAFS still leads when competitive encoders are used.\n- W4 Following up on S4. Including multi-model forecasting (i.e., ensembles) in the figure would provide the entire picture.\n\n\n**Minor concerns**\n- **Figure 5** is barely readable, and the green curve is hard to distinguish. Consider a shared legend, fewer plots in the main text (move the rest to the appendix), and larger fonts.\n    \n- **Figure 6**: spell out _Heterogeneous_ and _Homogeneous_.\n\n\n**Overall assessment**\nThe method is sensible, but the experimental comparison skips equally expressive ensemble baselines and alternative backbones, so the quantitative claims aren’t fully substantiated. The writing is mostly clear and the figures are well crafted. MAFS is potentially valuable, yet incremental relative to MoE/ensemble literature; its impact hinges on demonstrating backbone-agnostic benefits. While applying a multi-agent lens to time-series forecasting is interesting, the ingredients—task splitting, GCN message passing, confidence-weighted voting—are familiar.\n\nQUESTIONS:\n- Q1. Relation to hierarchical and multi-resolution forecasting: Could your homogeneous horizon split be viewed as a special hierarchical forecast (e.g., TimeMixer’s coarse-to-fine stages)? How would MAFS handle reconciliation constraints in hierarchical series?\n- Q2 \"Each agent is assigned a distinct sub-task (e.g., different signal characteristics)\" Do the sub-tasks change per agent across the training? If not do tasks ever migrate between agents during training. Would this be beneficial for generalization or overfitting on a task?\n- Q3 Please see W4, could you provide your framework for the different baselines with different enncoders.\n- Q4 Could you provide a Comparison to MoE / bagging, i.e. could you add a strong MoE baseline (e.g., Time-MoE) and a simple ensemble of K independently initialised iTransformers with learned linear weighting. How much of the gain comes purely from ensembling?\n- Q5 I don't think your ablation is sufficient, it would be significant to see how beneficial certain aspects are in isolation, i.e. the message-passing communication in an ensemble (w/o STS)\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-11"}