基于创新表示的时序大模型，用于工程实时监控与控制
AI Foundation Model for Time Series with Innovations Representation
- 用创新表示理论构建时序生成预训练模型TS-GPT
- 在美独立系统运营商数据上实现电价实时预测
- 适合需要物理规律建模的工程时序任务
本文提出面向工程应用的时序人工智能基础模型，针对实时监测与控制中需满足因果性的需求。由于工程时序受物理规律而非语言规律支配，基于大语言模型的AI基础模型可能无效或低效。基于Wiener、Kallianpur和Rosenblatt的经典创新表示理论，我们提出时序生成预训练变换器TS-GPT，用于工程监测与控制。作为基础模型适配的范例，我们研究了概率生成预测，即从给定历史实现的条件概率分布中生成未来时序样本。我们在美国独立系统运营商的历史数据上验证了TS-GPT在实时节点边际电价预测中的有效性。
原文摘要 · Abstract (English)
This paper introduces an Artificial Intelligence (AI) foundation model for time series in engineering applications, where causal operations are required for real-time monitoring and control. Since engineering time series are governed by physical, rather than linguistic, laws, large-language-model-based AI foundation models may be ineffective or inefficient. Building on the classical innovations representation theory of Wiener, Kallianpur, and Rosenblatt, we propose Time Series GPT (TS-GPT) -- an innovations-representation-based Generative Pre-trained Transformer for engineering monitoring and control. As an example of foundation model adaptation, we consider Probabilistic Generative Forecasting, which produces future time series samples from conditional probability distributions given past realizations. We demonstrate the effectiveness of TS-GPT in forecasting real-time locational marginal prices using historical data from U.S. independent system operators.
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