Financial applications such as stock price forecasting, usually face an issue that under the predefined labeling rules, it is hard to accurately predict the directions of stock movement. This is because traditional ways of labeling, taking Triple Barrier Method, for example, usually gives us inaccurate or even corrupted labels. To address this issue, we focus on two main goals. One is that our proposed method can automatically generate correct labels for noisy time series patterns, while at the same time, the method is capable of boosting classification performance on this new labeled dataset. Based on the aforementioned goals, our approach has the following three novelties: First, we fuse a new contrastive learning algorithm into the meta-learning framework to estimate correct labels iteratively when updating the classification model inside. Moreover, we utilize images generated from time series data through Gramian angular field and representative learning. Most important of all, we adopt multi-task learning to forecast temporal-variant labels. In the experiments, we work on 6% clean data and the rest unlabeled data. It is shown that our method is competitive and outperforms a lot compared with benchmarks.

翻译：金融应用（如股价预测）常面临一个问题：在预定义的标签规则下，难以准确预测股票走势的方向。这是因为传统标签方法（例如三重屏障法）往往会产生不准确甚至错误的标签。为解决这一问题，我们聚焦于两个主要目标：一是使所提方法能够自动为含噪声的时间序列模式生成正确标签，二是该方法同时能提升基于新标注数据集的分类性能。基于上述目标，我们的方法具有以下三点创新：首先，我们将一种新的对比学习算法融入元学习框架中，在内部更新分类模型时迭代估计正确标签。其次，我们利用通过格拉米角场和表征学习从时间序列数据生成的图像。最重要的是，我们采用多任务学习来预测时变标签。在实验中，我们使用6%的干净数据及剩余未标注数据进行训练。结果表明，我们的方法具有竞争力，且相比基准方法性能显著提升。