Early time series classification predicts the class label of a given time series before it is
completely observed. In time-critical applications, such as arrhythmia monitoring in ICU …

Dynamic inference is an emerging technique that reduces the computational cost of deep
neural network under resource-constrained scenarios, such as inference on mobile devices …

In recent years, early classification on time series has become increasingly important in time-
sensitive applications. Existing shapelet based methods still cannot work well on this …

Early classification tasks aim to classify time series before observing full data. It is critical in
time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU) …

Early classification algorithms help users react faster to their machine learning model's
predictions. Early warning systems in hospitals, for example, let clinicians improve their …

Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain
activity, how can we predict their health outcomes as early as possible? Early decision …

Electrocardiography (ECG) signals can be considered as multivariable time series (TS). The
state-of-the-art ECG data classification approaches, based on either feature engineering or …

Early time series classification is a variant of the time series classification task, in which a
label must be assigned to the incoming time series as quickly as possible without …

Early detection of infectious diseases such as Covid-19 can limit transmission and curb
pandemics. This study proposes EarlyDetect, an end-to-end framework for early Covid-19 …

Anomaly prediction, aiming to predict abnormal events before occurrence, plays a key role
in significantly reducing costs and minimizing potential threats to mechanical devices …