Robust Classification of Incomplete Time Series with Noisy Labels

Missing data and noisy labeling are common problems in time series analysis. The traditional approach to deal with missing data is to separate interpolation and classification, which is not interactive and provides unsatisfactory performance. While advanced methods can learn features from missing information, feature representation is limited due to the accumulation of interpolation errors. For noisy label interference, a robust loss function is a simpler and more general solution for robust learning. This study proposes an end-to-end neural network that unifies data interpolation and feature learning within a single framework. The focus is placed on extracting useful information from incomplete time series data, and for the computation of classification loss, a robustness loss function is used which effectively reduces the impact of noisy labels. The model is evaluated on 20 univariate time series from the UCR archive after noise processing. The results show that the model outperforms state-of-the-art methods in classifying incomplete time series under noisy labels, especially at high missing rates with high noise rates.