MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters

• URL: http://arxiv.org/abs/2608.23473v1
• Date: Mon, 24 Aug 2026 16:40:25 GMT
• Title: MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
• Abstract summary: Time series forecasting (TSF) is evolving toward multimodal and agentic settings.<n>We propose MetaCaster, a meta-harness-optimized multi-agent framework to automatically train specialized lightweight forecasters.<n>Our work highlights a new TSF paradigm in which agents act not as forecasters but as intermediary engineers.
• Score: 86.78565015172455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact, specialized forecasters are more desirable. However, lightweight forecasters typically require substantial training data, limiting their use in domains with scarce, slowly accumulated, or privacy-sensitive time series. To address this dilemma, we investigate the challenging problem of few-shot learning for lightweight forecasters. We propose MetaCaster, a meta-harness-optimized multi-agent framework that uses agentic data generation to automatically train specialized lightweight forecasters from only a few examples and textual contexts. Our work highlights a new TSF paradigm in which agents act not as forecasters but as intermediary engineers that prepare efficient, task-specific forecasters for deployment. Experiments on 18 datasets, 23 state-of-the-art lightweight forecasters, and 14 baselines demonstrate that MetaCaster achieves both data efficiency and computational efficiency while maintaining high-quality TSF performance.

Related papers

• Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model [1.7674345486888503]
  Chronos-2 is a representative multi-channel time-series foundation model (TSFM) developed by Amazon.<n>We evaluate Chronos-2 on two real-world utility datasets, ISO New England and ENTSO-E, and benchmark it against widely used task-specific deep learning models.<n>Our results show that Chronos-2 benefits substantially from task-specific fine-tuning and achieves strong short-horizon forecasting performance.
  arXiv  Detail & Related papers  (2026-09-06T15:09:12Z)
• KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning [25.103984906343328]
  Cross-domain multimodal time series forecasting is a challenging task, requiring models to integrate precise numerical comprehension, cross-domain semantic understanding, and effective multimodal fusion.<n>Existing approaches either build Time Series Foundation Models (TSFMs) from scratch or leverage pretrained Large Language Models (LLMs)<n>We propose KairosAgent, a novel agentic framework for multimodal time series forecasting, including an LLM-based reasoner and a TSFM-based forecaster.
  arXiv  Detail & Related papers  (2026-05-28T14:32:48Z)
• Nexus : An Agentic Framework for Time Series Forecasting [72.73790673303154]
  Time series forecasting requires reasoning with unstructured contextual data such as news or events.<n>We introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages.<n>We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized.
  arXiv  Detail & Related papers  (2026-05-14T05:12:13Z)
• Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling [54.94692733670454]
  Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles.<n>We propose a lightweight yet highly accurate streaming-based trajectory forecasting approach.<n>Our approach significantly reduces inference latency, making it well-suited for real-world deployment.
  arXiv  Detail & Related papers  (2026-03-02T13:44:23Z)
• A Lightweight Multi-View Approach to Short-Term Load Forecasting [44.00037058636877]
  Time series forecasting is a critical task across domains such as energy, finance, and meteorology.<n>We propose a lightweight multi-view approach to short-term load forecasting that leverages single-value embeddings and a scaled time-range input.
  arXiv  Detail & Related papers  (2026-02-09T21:43:42Z)
• TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis [25.377586527585503]
  TimeSeriesScientist (TSci) is a general, domain-agnostic framework for time series forecasting.<n>It reduces forecast error by an average of 10.4% and 38.2%, respectively.<n>With transparent natural-language rationales and comprehensive reports, TSci transforms the forecasting into a white-box system.
  arXiv  Detail & Related papers  (2025-10-02T00:18:59Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings [0.0]
  patio-temporal forecasting is crucial in transportation, logistics, and supply chain management. We propose a dynamic, multi-modal approach that integrates the strengths of traditional forecasting methods and instruction tuning of small language models. Our framework enables on-premises customization with reduced computational and memory demands, while maintaining inference speed and data privacy/security.
  arXiv  Detail & Related papers  (2024-08-24T16:32:58Z)
• Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models [68.23649978697027]
  Forecast-PEFT is a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks. Forecast-FT further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods.
  arXiv  Detail & Related papers  (2024-07-28T19:18:59Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.