mlsanityData to 7 Oct 2026

Efficient Model Selection for Time Series Forecasting via LLMs

arXiv 2504.02119 ↗First posted 2 Apr 2025cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Model selection is a critical step in time series forecasting, traditionally requiring extensive performance evaluations across various datasets. Meta-learning approaches aim to automate this process, but they typically depend on pre-constructed performance matrices, which are costly to build. In this work, we propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs. Through extensive experiments with LLaMA, GPT and Gemini, we demonstrate that our approach outperforms traditional meta-learning techniques and heuristic baselines, while significantly reducing computational overhead. These findings underscore the potential of LLMs in efficient model selection for time series forecasting.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 2 Apr 2025.

Builds on

11 of 33 references.

References · 11

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2024GPT-4o System Cardcited by 4,467
  3. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  4. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  5. 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 270
  6. 2018Meta-Learning: A Surveycited by 254
  7. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  8. 2023A Practical Survey on Zero-shot Prompt Design for In-context Learningcited by 49
  9. 2020Automating Outlier Detection via Meta-Learningcited by 9
  10. 2024Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecastingcited by 5
  11. 2024Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selectioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Estimating Time Series Foundation Model Transferability via In-Context Learningcited by 2
  2. 2026Experiments in Agentic AI for Sciencecited by 0
  3. 2026When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasterscited by 0
  4. 2026Large Models for Battery Prognostics and Health Management: A Review and Future Roadmapcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Rethinking the Role of LLMs in Time Series Forecasting
  2. 2025Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
  3. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications
  4. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
  5. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
  6. 2024AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
  7. 2025Context information can be more important than reasoning for time series forecasting with a large language model
  8. 2024TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation