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

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 …

In many real-world applications, ranging from predictive maintenance to personalized
medicine, early classification of time series data is of paramount importance for supporting …

An increasing number of applications require to recognize the class of an incoming time
series as quickly as possible without unduly compromising the accuracy of the prediction. In …

More and more applications require early decisions, ie taken as soon as possible from
partially observed data. However, the later a decision is made, the more its accuracy tends …

In time series classification, two antagonist notions are at stake. On the one hand, in most
cases, the sooner the time series is classified, the more rewarding. On the other hand, an …

In many time-sensitive applications, knowing the classification results as early as possible
while preserving the accuracy is extremely important for further actions. Shapelet-based …

We propose a model for multiclass classification of time series to make a prediction as early
and as accurate as possible. The matrix sequential probability ratio test (MSPRT) is known …

Many approaches have been proposed for early classification of time series in light of its
significance in a wide range of applications including healthcare, transportation and finance …

Early classification of time series assigns each time series to one of a set of pre-defined
classes using as few measurements as possible while preserving a high accuracy. This …