Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting

TL;DR

This paper introduces LLMPred, a novel approach that converts time-series data into text to leverage LLMs for zero-shot forecasting, effectively handling complex and multivariate sequences with minimal training.

Contribution

The paper presents a new method that extends LLMs for multivariate time-series forecasting using text conversion and prompt strategies, demonstrating competitive performance.

Findings

LLMPred achieves comparable or better results than state-of-the-art baselines.

Sequence decomposition improves prediction accuracy on noisy data.

Prompt-processing enables multivariate forecasting with lightweight LLMs.

Abstract

Time-series prediction or forecasting is critical across many real-world dynamic systems, and recent studies have proposed using Large Language Models (LLMs) for this task due to their strong generalization capabilities and ability to perform well without extensive pre-training. However, their effectiveness in handling complex, noisy, and multivariate time-series data remains underexplored. To address this, we propose LLMPred which enhances LLM-based time-series prediction by converting time-series sequences into text and feeding them to LLMs for zero shot prediction along with two main data pre-processing techniques. First, we apply time-series sequence decomposition to facilitate accurate prediction on complex and noisy univariate sequences. Second, we extend this univariate prediction capability to multivariate data using a lightweight prompt-processing strategy. Extensive experiments with smaller LLMs such as Llama 2 7B, Llama 3.2 3B, GPT-4o-mini, and DeepSeek 7B demonstrate that LLMPred achieves competitive or superior performance compared to state-of-the-art baselines. Additionally, a thorough ablation study highlights the importance of the key components proposed in LLMPred.