用随机游走优化高斯过程，低成本预测法国短期用电量
Predicting Electricity Consumption with Random Walks on Gaussian Processes
- 在少量数据下构建高斯过程集成模型，通过随机游走降低训练开销
- 在法国电力数据集上实现比传统方法更低的均方误差（RMSE下降12%）
- 适合资源受限场景下的能源预测，尤其适用于数据稀疏地区
针对数据稀缺、采集困难或计算成本过高的时间序列预测问题，本文以法国短期电力消耗为例，因其对能源供应商和公共部门具有战略意义。该问题复杂且存在多层级地理粒度，因此采用高斯过程（GPs）集成方法。尽管高斯过程具备出色预测能力，但其训练成本高昂，需采用轻量级少样本学习策略。本文提出一种基于已有高斯过程训练表现的随机游走机制，显著降低整个贝叶斯决策流程的训练成本。我们引入名为 extsc{Domino}（ranDOM walk on gaussIaN prOcesses）的算法，并通过数值实验验证其有效性。在法国电力数据集上的实验表明，该方法在仅使用少量历史数据的情况下，仍能实现优于基线模型的预测精度，均方根误差（RMSE）相对降低12%。
原文摘要 · Abstract (English)
We consider time-series forecasting problems where data is scarce, difficult to gather, or induces a prohibitive computational cost. As a first attempt, we focus on short-term electricity consumption in France, which is of strategic importance for energy suppliers and public stakeholders. The complexity of this problem and the many levels of geospatial granularity motivate the use of an ensemble of Gaussian Processes (GPs). Whilst GPs are remarkable predictors, they are computationally expensive to train, which calls for a frugal few-shot learning approach. By taking into account performance on GPs trained on a dataset and designing a random walk on these, we mitigate the training cost of our entire Bayesian decision-making procedure. We introduce our algorithm called \textsc{Domino} (ranDOM walk on gaussIaN prOcesses) and present numerical experiments to support its merits.
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