标题： 滑动窗口签名用于时间序列：电力需求预测的应用 显示英文标题

标题： Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting

摘要： 协变量的非线性和延迟效应常常使时间序列预测变得具有挑战性。 为此，我们提出了一种基于岭回归的新型预测框架，该框架在滑动窗口上计算签名特征。 这些特征能够在不依赖于学习或手工设计表示的情况下捕捉复杂的时序动态。 专注于离散时间设置，我们建立了理论保证，即逼近的普遍性和签名的平稳性。 我们引入了一种高效的顺序算法，用于在滑动窗口上计算签名。 该方法在合成和真实电力需求数据上进行了评估。 结果表明，签名特征能够有效编码时间和非线性依赖关系，产生与基于专家知识的预测相媲美的准确预测。 显示英文摘要

摘要： Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with signature features calculated on sliding windows. These features capture complex temporal dynamics without relying on learned or hand-crafted representations. Focusing on the discrete-time setting, we establish theoretical guarantees, namely universality of approximation and stationarity of signatures. We introduce an efficient sequential algorithm for computing signatures on sliding windows. The method is evaluated on both synthetic and real electricity demand data. Results show that signature features effectively encode temporal and nonlinear dependencies, yielding accurate forecasts competitive with those based on expert knowledge.