The effectiveness of univariate forecasting models is often hampered by
conditions that cause them stress. A model is considered to be under stress if
it shows a negative behaviour, such as higher-than-usual errors or increased
uncertainty. Understanding the factors that cause stress to forecasting models
is important to improve their reliability, transparency, and utility. This
paper addresses this problem by contributing with a novel framework called MAST
(Meta-learning and data Augmentation for Stress Testing). The proposed approach
aims to model and characterize stress in univariate time series forecasting
models, focusing on conditions where they exhibit large errors. In particular,
MAST is a meta-learning approach that predicts the probability that a given
model will perform poorly on a given time series based on a set of statistical
time series features. MAST also encompasses a novel data augmentation technique
based on oversampling to improve the metadata concerning stress. We conducted
experiments using three benchmark datasets that contain a total of 49.794 time
series to validate the performance of MAST. The results suggest that the
proposed approach is able to identify conditions that lead to large errors. The
method and experiments are publicly available in a repository.

本文提出了一种名为 MAST（元学习和数据增强用于压力测试）的新型框架，旨在对单变量时间序列预测模型中的压力进行建模和表征，重点关注其在出现大误差的条件下的表现；该方法基于一组统计时间序列特征，通过元学习预测给定时间序列上给定模型表现不佳的概率，同时还包括一种基于过采样的新型数据增强技术，以改进有关压力的元数据。通过使用包含 49,794 个时间序列的三个基准数据集进行实验证明，该方法能够识别导致大误差的条件。