ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges when applied to high-dimensional and domain-speci...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/7/2/35 |
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| author | Jinglei Pei Yang Zhang Ting Liu Jingbin Yang Qinghua Wu Kang Qin |
| author_facet | Jinglei Pei Yang Zhang Ting Liu Jingbin Yang Qinghua Wu Kang Qin |
| author_sort | Jinglei Pei |
| collection | DOAJ |
| description | Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges when applied to high-dimensional and domain-specific datasets. These challenges primarily arise from inability to effectively model inter-variable dependencies and capture variable-specific characteristics, leading to suboptimal performance in complex forecasting scenarios. To address these limitations, we propose <b>ADTime</b>, an adaptive LLM-based approach for multivariate time series forecasting. ADTime employs advanced preprocessing techniques to identify latent relationships among key variables and temporal features. Additionally, it integrates temporal alignment mechanisms and prompt-based strategies to enhance the semantic understanding of forecasting tasks by LLMs. Experimental results show that ADTime outperforms state-of-the-art methods, reducing MSE by 9.5% and MAE by 6.1% on public datasets, and by 17.1% and 13.5% on domain-specific datasets. Furthermore, zero-shot experiments on real-world refinery datasets demonstrate that ADTime exhibits stronger generalization capabilities across various transfer scenarios. These findings highlight the potential of ADTime in advancing complex, domain-specific time series forecasting tasks. |
| format | Article |
| id | doaj-art-bfe7620103b44be28a94e4ff5c21a8ec |
| institution | Kabale University |
| issn | 2504-4990 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-bfe7620103b44be28a94e4ff5c21a8ec2025-08-20T03:27:29ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902025-04-01723510.3390/make7020035ADTime: Adaptive Multivariate Time Series Forecasting Using LLMsJinglei Pei0Yang Zhang1Ting Liu2Jingbin Yang3Qinghua Wu4Kang Qin5Institute of Computing Technology, University of Chinese Academy of Sciences, Beijing 100190, ChinaResearch Institute of Petroleum Processing, SINOPEC, Beijing 100083, ChinaInstitute of Computing Technology, University of Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Computing Technology, University of Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Computing Technology, University of Chinese Academy of Sciences, Beijing 100190, ChinaResearch Institute of Petroleum Processing, SINOPEC, Beijing 100083, ChinaLarge language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges when applied to high-dimensional and domain-specific datasets. These challenges primarily arise from inability to effectively model inter-variable dependencies and capture variable-specific characteristics, leading to suboptimal performance in complex forecasting scenarios. To address these limitations, we propose <b>ADTime</b>, an adaptive LLM-based approach for multivariate time series forecasting. ADTime employs advanced preprocessing techniques to identify latent relationships among key variables and temporal features. Additionally, it integrates temporal alignment mechanisms and prompt-based strategies to enhance the semantic understanding of forecasting tasks by LLMs. Experimental results show that ADTime outperforms state-of-the-art methods, reducing MSE by 9.5% and MAE by 6.1% on public datasets, and by 17.1% and 13.5% on domain-specific datasets. Furthermore, zero-shot experiments on real-world refinery datasets demonstrate that ADTime exhibits stronger generalization capabilities across various transfer scenarios. These findings highlight the potential of ADTime in advancing complex, domain-specific time series forecasting tasks.https://www.mdpi.com/2504-4990/7/2/35time seriestime series forecastinglarge language models |
| spellingShingle | Jinglei Pei Yang Zhang Ting Liu Jingbin Yang Qinghua Wu Kang Qin ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs Machine Learning and Knowledge Extraction time series time series forecasting large language models |
| title | ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs |
| title_full | ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs |
| title_fullStr | ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs |
| title_full_unstemmed | ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs |
| title_short | ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs |
| title_sort | adtime adaptive multivariate time series forecasting using llms |
| topic | time series time series forecasting large language models |
| url | https://www.mdpi.com/2504-4990/7/2/35 |
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