The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Early classification of time series has been extensively studied for minimizing class
prediction delay in time-sensitive applications such as medical diagnostic and industrial …

Early time series classification (eTSC) is the problem of classifying a time series after as few
measurements as possible with the highest possible accuracy. The most critical issue of any …

The problem of early classification of time series appears naturally in contexts where the
data, of temporal nature, are collected over time, and early class predictions are interesting …

The goal of early classification of time series is to predict the class value of a sequence early
in time, when its full length is not yet available. This problem arises naturally in many …

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 …

Classification of time series as early as possible is a valuable goal. Indeed, in many
application domains, the earliest the decision, the more rewarding it can be. Yet, often …

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 …

Early classification of time series is the prediction of the class label of a time series before it
is observed in its entirety. In time-sensitive domains where information is collected over time …

In early classification of time series the objective is to build models which are able to make
class-predictions for time series as accurately and as early as possible, when only a part of …