  In the real world, the class of a time series is usually labeled at the final
time, but many applications require to classify time series at every time
point. e.g. the outcome of a critical patient is only determined at the end,
but he should be diagnosed at all times for timely treatment. Thus, we propose
a new concept: Continuous Classification of Time Series (CCTS). It requires the
model to learn data in different time stages. But the time series evolves
dynamically, leading to different data distributions. When a model learns
multi-distribution, it always forgets or overfits. We suggest that meaningful
learning scheduling is potential due to an interesting observation: Measured by
confidence, the process of model learning multiple distributions is similar to
the process of human learning multiple knowledge. Thus, we propose a novel
Confidence-guided method for CCTS (C3TS). It can imitate the alternating human
confidence described by the Dunning-Kruger Effect. We define the objective-
confidence to arrange data, and the self-confidence to control the learning
duration. Experiments on four real-world datasets show that C3TS is more
accurate than all baselines for CCTS.

Machine Learning