This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from the historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well-known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, in generating a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides provides comparable performances to other model selection/combination approaches but at lower computational cost and higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, how the meta-learners work and how the forecasting performances are affected by various factors.

翻译：本文为时间序列预测模型性能预测引入了一种新的元学习算法。我们用一种高效的贝叶西亚多变量表面回归法,将预测错误作为从历史时间序列中计算的时间序列功能的函数进行模型计算。然后使用最低预测错误来确定单个模型或模型组合,以得出最后预测结果。众所周知,大多数元学习模型的性能取决于用于培训的参考数据集的代表性。在这种情况下,我们用基于特征的时间序列模拟方法,即GRATIS,来增加参考数据集,以产生丰富和有代表性的时间序列。拟议框架使用M4竞争数据进行测试,并与常用的预测方法进行比较。我们的方法提供了与其他模型选择/组合方法的可比较性能,但计算成本较低,解释程度更高,这对支持决定十分重要。我们还就预测模型在特定时间序列中预期哪些效果更好、元Lears如何工作以及预测业绩如何受到各种因素的影响等问题提供了有益的见解。