Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
This paper examines the relationship among CO2 emissions, energy use, and GDP in Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses …
A Sardá-Espinosa - R package vignette, 2017 - cran.radicaldevelop.com
Most clustering strategies have not changed considerably since their initial definition. The common improvements are either related to the distance measure used to assess …
Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial …
RV Kübler, A Colicev… - Journal of Interactive …, 2020 - journals.sagepub.com
User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and …
L He, B Agard, M Trépanier - Transportmetrica A: Transport …, 2020 - Taylor & Francis
ABSTRACT A classification of the behavior of smart card users is important in the field of public transit demand analysis. It provides an understanding of people's sequence of …
Time series classification has been an important and challenging research task. In different domains, time series show different patterns, which makes it difficult to design a global …
S Guan, B Zhao, Z Dong, M Gao, Z He - Entropy, 2022 - mdpi.com
The rapid development of smart factories, combined with the increasing complexity of production equipment, has resulted in a large number of multivariate time series that can be …
Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a …