# MultiCast: Zero-Shot Multivariate Time Series Forecasting Using LLMs

arXiv: https://arxiv.org/abs/2405.14748 · HTML: https://mlsanity.com/paper/2405.14748/

## 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 (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://mlsanity.com/paper/2210.08964/) (2022)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Large Language Models for Time Series: A Survey](https://mlsanity.com/paper/2402.01801/) (2024)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [Empowering Time Series Analysis with Large Language Models: A Survey](https://mlsanity.com/paper/2402.03182/) (2024)
- [Transferability in Deep Learning: A Survey](https://mlsanity.com/paper/2201.05867/) (2022)
- [Zero-Shot Clinical Trial Patient Matching with LLMs](https://mlsanity.com/paper/2402.05125/) (2024)
- [LLM on FHIR -- Demystifying Health Records](https://mlsanity.com/paper/2402.01711/) (2024)

## Built on by (most cited first, 6 of 6)

- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [CaTS-Bench: Can Language Models Describe Time Series?](https://mlsanity.com/paper/2509.20823/) (2025)
- [Unveiling the Potential of Text in High-Dimensional Time Series Forecasting](https://mlsanity.com/paper/2501.07048/) (2025)
- [TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction](https://mlsanity.com/paper/2510.01111/) (2025)
- [Zero and Few Shot Load Forecasting with Large Language Models](https://mlsanity.com/paper/2411.11350/) (2024)
- [Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models](https://mlsanity.com/paper/2501.11247/) (2025)

## 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.