Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework. There are three main contributions. First, we train the label correction model with a two-branch neural network for the outer loop. While in the model-agnostic inner loop, we use pre-existing classification models in a multi-task way and jointly update the meta-knowledge, which makes us achieve adaptive labeling on complex time series. Second, we devise new data visualization methods for both image patterns of the historical data and data in the prediction horizon. Finally, we test our method with various financial datasets, including XOM, S\&P500, and SZ50. Results show that our method is more effective and accurate than some existing label correction techniques.

翻译：时间序列分类面临两个不可避免的问题：一是特征信息不完整，二是标签质量不佳，这些都会影响模型性能。为解决上述问题，本文在多任务框架下结合元学习创建了一种面向时间序列数据的标签修正方法。主要贡献体现在三个方面：首先，我们利用双分支神经网络在外部循环中训练标签修正模型；而在与模型无关的内部循环中，我们以多任务方式使用预训练分类模型并联合更新元知识，从而实现对复杂时间序列的自适应标记。其次，我们针对历史数据的图像模式与预测域中的数据设计了新的可视化方法。最后，我们在包括XOM、S&P500和SZ50在内的多个金融数据集上测试了所提方法。结果表明，与现有部分标签修正技术相比，本方法具有更优的有效性和准确性。