Accuracy is a key focus of current work in time series classification. However, speed and data reduction are equally important many applications, especially when the scale storage requirements rapidly increase. Current multivariate classification (MTSC) algorithms need hundreds compute hours to complete training prediction. This due nature which grows with number series, their length channels. ...

the purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. the article is based on the gas chromatography analysis of heroin samples seized from three different locations in serbia. chemometric approach with appropriate statistical tools (multip...

Shapelets are phase independent subsequences designed for time series classification. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features in multivariate time series classification. We create a unified set of data to benchmark our work on, and compare with three other algorithms. We demonstrate that multivariate shapelets are not significantly worse than...

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...

The purpose of this work is to promote and facilitate forensic profiling and chemical analysis of illicit drug samples in order to determine their origin, methods of production and transfer through the country. The article is based on the gas chromatography analysis of heroin samples seized from three different locations in Serbia. Chemometric approach with appropriate statistical tools (multip...