The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

A highly comparative, feature-based approach to time series classification is introduced that
uses an extensive database of algorithms to extract thousands of interpretable features from …

The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …

In this paper, we propose a novel method for discovering characteristic patterns in a time
series called SAX-VSM. This method is based on two existing techniques-Symbolic …

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring
physiological signals while driving provides the possibility of detecting and warning of …

There has been much recent interest in adapting data mining algorithms to time series
databases. Most of these algorithms need to compare time series. Typically some variation …

In this paper, we propose some new tools to allow machine learning classifiers to cope with
time series data. We first argue that many time-series classification problems can be solved …

Chemical process plant safety, production specifications, environmental regulations,
operational constraints, and plant economics are some of the main reasons driving an …

1 Introduction Time series are a ubiquitous form of data occurring in virtually every scientific
discipline and business application. There has been much recent work on adapting data …

Abstract Machine learning research has, to a great extent, ignored an important aspect of
many real world applications: time. Existing concept learners predominantly operate on a …