Meta learning is a promising technique for solving few-shot fault prediction
problems, which have attracted the attention of many researchers in recent
years. Existing meta-learning methods for time series prediction, which
predominantly rely on random and similarity matching-based task partitioning,
face three major limitations: (1) feature exploitation inefficiency; (2)
suboptimal task data allocation; and (3) limited robustness with small samples.
To overcome these limitations, we introduce a novel 'pseudo meta-task'
partitioning scheme that treats a continuous time period of a time series as a
meta-task, composed of multiple successive short time periods. Employing
continuous time series as pseudo meta-tasks allows our method to extract more
comprehensive features and relationships from the data, resulting in more
accurate predictions. Moreover, we introduce a differential algorithm to
enhance the robustness of our method across different datasets. Through
extensive experiments on several fault and time series prediction datasets, we
demonstrate that our approach substantially enhances prediction performance and
generalization capability under both few-shot and general conditions.

通过引入一种新颖的 “伪元任务” 分割方案，将连续时间序列作为伪元任务，使我们的方法从数据中提取更全面的特征和关系，从而实现更准确的预测。此外，我们引入了一种差分算法来增强我们的方法在不同数据集上的鲁棒性。通过对几个故障和时间序列预测数据集进行广泛实验，我们证明了我们的方法在少样本和一般条件下显著提高了预测性能和泛化能力。