论文标题
基于预测电价的变更点检测的校准窗口选择
Calibration window selection based on change-point detection for forecasting electricity prices
论文作者
论文摘要
我们采用最近提出的更改点检测算法，即最狭窄的阈值（非）方法，以选择与当前记录的值相似的过去观察值的子周期。然后，与传统的时间序列方法相反，最新的$τ$观测值是校准样本，我们仅估计这些子周期中数据的自回归模型。我们使用具有挑战性的数据集（德国EPEX现货市场中的日用电价）说明了我们的方法，并观察到与常用方法相比，预测准确性有了显着提高，包括自动回归杂种最近的邻居（ARHNN）方法。
We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values. Then, contrarily to the traditional time series approach in which the most recent $τ$ observations are taken as the calibration sample, we estimate autoregressive models only for data in these subperiods. We illustrate our approach using a challenging dataset - day-ahead electricity prices in the German EPEX SPOT market - and observe a significant improvement in forecasting accuracy compared to commonly used approaches, including the Autoregressive Hybrid Nearest Neighbors (ARHNN) method.
