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