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：股票价格预测等金融应用通常面临一个问题,根据预先定义的标签规则,很难准确预测股票流动的方向。 这是因为传统的标签方法,例如采用三重障碍法,通常给我们提供不准确甚至腐败的标签。 为了解决这个问题,我们集中关注两个主要目标。 其中之一是我们提议的方法可以自动生成噪音时间序列模式的正确标签,而与此同时,这种方法能够提高这个新的标签数据集的分类性能。 根据上述目标,我们的方法有以下三个新颖之处:首先,我们将新的对比式学习算法纳入元学习框架,以便在更新内部分类模型时反复估计正确的标签。此外,我们利用时间序列数据产生的图像,通过格拉姆角字段和代表性学习。最重要的是,我们采用多功能学习来预测时间变异性标签。在实验中,我们研究6%的清洁数据和其余的无标签数据。它表明,我们的方法具有竞争力,比基准要高出很多。</s>