mlsanityData to 14 Oct 2026

MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs

arXiv 2405.14748 ↗First posted 23 May 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

Predicting future values in multivariate time series is vital across various domains. This work explores the use of large language models (LLMs) for this task. However, LLMs typically handle one-dimensional data. We introduce MultiCast, a zero-shot LLM-based approach for multivariate time series forecasting. It allows LLMs to receive multivariate time series as input, through three novel token multiplexing solutions that effectively reduce dimensionality while preserving key repetitive patterns. Additionally, a quantization scheme helps LLMs to better learn these patterns, while significantly reducing token use for practical applications. We showcase the performance of our approach in terms of RMSE and execution time against state-of-the-art approaches on three real-world datasets.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

16 of 35 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2023A Survey of Large Language Modelscited by 2,576
  3. 2022Emergent Abilities of Large Language Modelscited by 2,512
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  6. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  7. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
  8. 2023Large Language Models in Finance: A Surveycited by 226
  9. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 172
  10. 2020Deep Learning for Time Series Forecasting: Tutorial and Literature Surveycited by 117
  11. 2024Large Language Models for Time Series: A Surveycited by 114
  12. 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 90
4 more
  1. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  2. 2022Transferability in Deep Learning: A Surveycited by 57
  3. 2024Zero-Shot Clinical Trial Patient Matching with LLMscited by 35
  4. 2024LLM on FHIR -- Demystifying Health Recordscited by 9

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Cited by year

Citing papers per year: 2024: 1; 2025: 5.

6 papers.

  1. 2025A Survey of AIOps in the Era of Large Language Modelscited by 19
  2. 2025CaTS-Bench: Can Language Models Describe Time Series?cited by 10
  3. 2025Unveiling the Potential of Text in High-Dimensional Time Series Forecastingcited by 4
  4. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Predictioncited by 4
  5. 2024Zero and Few Shot Load Forecasting with Large Language Modelscited by 0
  6. 2025Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Rethinking the Role of LLMs in Time Series Forecasting
  2. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
  3. 2025DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework
  4. 2025Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
  5. 2025Efficient Model Selection for Time Series Forecasting via LLMs
  6. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
  7. 2025Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs
  8. 2024MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual Unmixing