  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 in 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 so as to help 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.
