论文标题
基于双级编程的自动化少数时间序列预测
Automated Few-Shot Time Series Forecasting based on Bi-level Programming
论文作者
论文摘要
具有可再生能源和电池存储系统的新微网格设计可以帮助改善温室气体排放并降低运营成本。为了提供对能源产生和负载需求的有效短期/长期预测，时间序列预测建模一直是指导最佳计划和操作决策的关键工具之一。时间序列可再生能源预测的关键挑战之一是缺乏培训足够预测模型的历史数据。此外，机器学习模型的性能对其相应的超参数的选择敏感。考虑到这些考虑因素，本文开发了一个Bilo-Auto-TSF/ML框架，该框架从双层编程的角度自动化了几次学习管道的最佳设计。具体而言，下层元学习有助于增强基础学习者以减轻小数据挑战，而在上层的超参数优化可主动搜索为基础和元学习者的最佳超参数配置。请注意，所提出的框架是如此一般，以至于任何现成的机器学习方法都可以以插件方式使用。全面的实验充分证明了我们提出的BILO-AUTO-TSF/ML框架在为各种能源寻找高性能的少量学习管道方面的有效性。
New micro-grid design with renewable energy sources and battery storage systems can help improve greenhouse gas emissions and reduce the operational cost. To provide an effective short-/long-term forecasting of both energy generation and load demand, time series predictive modeling has been one of the key tools to guide the optimal decision-making for planning and operation. One of the critical challenges of time series renewable energy forecasting is the lack of historical data to train an adequate predictive model. Moreover, the performance of a machine learning model is sensitive to the choice of its corresponding hyperparameters. Bearing these considerations in mind, this paper develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bi-level programming perspective. Specifically, the lower-level meta-learning helps boost the base-learner to mitigate the small data challenge while the hyperparameter optimization at the upper level proactively searches for the optimal hyperparameter configurations for both base- and meta-learners. Note that the proposed framework is so general that any off-the-shelf machine learning method can be used in a plug-in manner. Comprehensive experiments fully demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML framework to search for a high-performance few-shot learning pipeline for various energy sources.
