{"ID":2843100,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07777","arxiv_id":"2511.07777","title":"A Causal-Guided Multimodal Large Language Model for Generalized Power System Time-Series Data Analytics","abstract":"Power system time series analytics is critical in understanding the system operation conditions and predicting the future trends. Despite the wide adoption of Artificial Intelligence (AI) tools, many AI-based time series analytical models suffer from task-specificity (i.e. one model for one task) and structural rigidity (i.e. the input-output format is fixed), leading to limited model performances and resource wastes. In this paper, we propose a Causal-Guided Multimodal Large Language Model (CM-LLM) that can solve heterogeneous power system time-series analysis tasks. First, we introduce a physics-statistics combined causal discovery mechanism to capture the causal relationship, which is represented by graph, among power system variables. Second, we propose a multimodal data preprocessing framework that can encode and fuse text, graph and time series to enhance the model performance. Last, we formulate a generic \"mask-and-reconstruct\" paradigm and design a dynamic input-output padding mechanism to enable CM-LLM adaptive to heterogeneous time-series analysis tasks with varying sample lengths. Simulation results based on open-source LLM Qwen and real-world dataset demonstrate that, after simple fine-tuning, the proposed CM-LLM can achieve satisfying accuracy and efficiency on three heterogeneous time-series analytics tasks: missing data imputation, forecasting and super resolution.","short_abstract":"Power system time series analytics is critical in understanding the system operation conditions and predicting the future trends. Despite the wide adoption of Artificial Intelligence (AI) tools, many AI-based time series analytical models suffer from task-specificity (i.e. one model for one task) and structural rigidit...","url_abs":"https://arxiv.org/abs/2511.07777","url_pdf":"https://arxiv.org/pdf/2511.07777v1","authors":"[\"Zhenghao Zhou\",\"Yiyan Li\",\"Xinjie Yu\",\"Runlong Liu\",\"Zelin Guo\",\"Zheng Yan\",\"Mo-Yuen Chow\",\"Yuqi Yang\",\"Yang Xu\"]","published":"2025-11-11T02:50:23Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}