用模糊因果图增强GPT-2，让时序预测更可解释。
Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
- 将时序数据模糊化并构建因果图，生成可读文本输入GPT-2
- 在4个多变量时序数据集上验证了模型预测有效性
- 适合关注可解释性时序预测的研究者
近年来，大型语言模型（LLM）在时序预测（TSF）中的应用受到广泛关注。本研究提出一种新型LLM框架CGF-LLM，结合GPT-2、模糊时间序列（FTS）与因果图，用于多变量时序预测，为该领域首个此类架构。核心目标是通过模糊化与因果分析并行处理，将数值时序转化为可解释的语义形式，使预训练GPT-2模型获得语义理解与结构洞察双重输入。由此生成的文本表示提供了对原始时序复杂动态的更可解释视角。实验在四个不同多变量时序数据集上验证了模型的有效性，为基于模糊时间序列的LLM时序预测开辟了新方向。
原文摘要 · Abstract (English)
In recent years, the application of Large Language Models (LLMs) to time series forecasting (TSF) has garnered significant attention among researchers. This study presents a new frame of LLMs named CGF-LLM using GPT-2 combined with fuzzy time series (FTS) and causal graph to predict multivariate time series, marking the first such architecture in the literature. The key objective is to convert numerical time series into interpretable forms through the parallel application of fuzzification and causal analysis, enabling both semantic understanding and structural insight as input for the pretrained GPT-2 model. The resulting textual representation offers a more interpretable view of the complex dynamics underlying the original time series. The reported results confirm the effectiveness of our proposed LLM-based time series forecasting model, as demonstrated across four different multivariate time series datasets. This initiative paves promising future directions in the domain of TSF using LLMs based on FTS.
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