A novel LLM time series forecasting method based on integer-decimal decomposition.
Journal:
Scientific reports
Published Date:
Jul 2, 2025
Abstract
The use of traditional deep learning models for time series forecasting has demonstrated strong performance in specific domains, but their applicability remains limited due to their domain-specific nature, which restricts generalization. Inspired by advancements in natural language processing (NLP) and computer vision (CV), large language models (LLMs) have emerged as a promising method for time series forecasting. However, fundamental differences between time series data and textual data present challenges in adapting time series for LLM-based forecasting. To address this, we propose an Integer-Decimal Decomposition and cross-modal fine-tuning framework for LLMs (IDDLLM). Our approach designs the Splitting time series Data Cross-attention (SDC) module, which decomposes time series data into integer and decimal components, enabling better correlation analysis and improving the model's understanding of time series patterns. Additionally, we design a dual cross-attention module to align time series modalities and text modalities, facilitating more effective adaptation of time series within LLMs. Comprehensive evaluations demonstrate that IDDLLM outperforms state-of-the-art models in long-term time series forecasting, ranking first in 34 out of 46 experimental settings and second in 9 settings. Furthermore, it achieves competitive performance in few-shot and zero-shot forecasting tasks, highlighting its robustness and adaptability.
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