Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …

The recent advances in hardware and software have enabled the capture of different
measurements of data in a wide range of fields. These measurements are generated …

We present sktime--a new scikit-learn compatible Python library with a unified interface for
machine learning with time series. Time series data gives rise to various distinct but closely …

Event labeling is the process of marking events in unlabeled data. Traditionally, this is done
by involving one or more human experts through an expensive and time-consuming task. In …

Change point analysis has applications in a wide variety of fields. The general problem
concerns the inference of a change in distribution for a set of time-ordered observations …

The microblogging site Twitter generates a constant stream of communication, some of
which concerns events of general interest. An analysis of Twitter may, therefore, give …

A fundamental problem in text data mining is to extract meaningful structure from document
streams that arrive continuously over time. E-mail and news articles are two natural …

The objective of change-point detection is to discover abrupt property changes lying behind
time-series data. In this paper, we present a novel statistical change-point detection …

In the last decade there has been an explosion of interest in mining time series data.
Literally hundreds of papers have introduced new algorithms to index, classify, cluster and …

As the world becomes increasingly connected, it is also more exposed to a myriad of cyber
threats. We need to use multiple types of tools and techniques to learn and understand the …