序贯学习设置中 COBRA 的若干变体
Some variation of COBRA in sequential learning setup
AI总结：
本文提出序贯学习中基于组合回归策略变体的多元时间序列预测方法，结合特定预处理，并比较贝叶斯优化与网格搜索，在三类八个数据集上超越现有模型。
摘要：
本研究论文提出了基于组合回归策略不同变体的多元时间序列预测创新方法。我们采用特定的数据预处理技术，使预测行为发生根本性改变。我们基于两类超参数调优方法——贝叶斯优化（BO）和常规网格搜索——比较模型性能。所提出的方法优于所有最先进的对比模型。我们通过来自加密货币、股票指数和短期负荷预测三个类别的八个时间序列数据集说明了这些方法。
英文摘要：
This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behaviour of prediction. We compare the performance of the model based on two types of hyper-parameter tuning Bayesian optimisation (BO) and Usual Grid search. Our proposed methodologies outperform all state-of-the-art comparative models. We illustrate the methodologies through eight time series datasets from three categories: cryptocurrency, stock index, and short-term load forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
