Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting

TL;DR

This paper introduces CGF-LLM, a novel architecture combining GPT-2 with fuzzy time series and causal graphs to improve interpretability and accuracy in multivariate time series forecasting.

Contribution

It is the first to integrate fuzzy time series and causal graphs with LLMs for time series forecasting, enhancing interpretability and structural understanding.

Findings

Effective across four multivariate datasets

Improves interpretability of time series models

Demonstrates promising future research directions

Abstract

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.