Classification on Time Series with Missing Data

"Classification on Time Series with Missing Data" refers to the task of classifying sequences using machine learning methods when the time series data contains missing values. The goal is to develop effective algorithms and models to handle incomplete data issues, thereby improving classification accuracy and robustness. This task has significant application value in fields such as financial forecasting, medical diagnosis, and equipment maintenance, helping decision-makers make more reliable judgments even when data is missing.