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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.17016 |
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| _version_ | 1866913955534864384 |
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| author | Orang, Omid Lucas, Patricia O. Paiva, Gabriel I. F. Silva, Petronio C. L. da Silva, Felipe Augusto Rocha Veloso, Adriano Alonso Guimaraes, Frederico Gadelha |
| author_facet | Orang, Omid Lucas, Patricia O. Paiva, Gabriel I. F. Silva, Petronio C. L. da Silva, Felipe Augusto Rocha Veloso, Adriano Alonso Guimaraes, Frederico Gadelha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting Orang, Omid Lucas, Patricia O. Paiva, Gabriel I. F. Silva, Petronio C. L. da Silva, Felipe Augusto Rocha Veloso, Adriano Alonso Guimaraes, Frederico Gadelha Machine Learning Artificial Intelligence 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. |
| title | Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.17016 |