Time series analysis via network science: Concepts and algorithms
There is nowadays a constant flux of data being generated and collected in all types of real
world systems. These data sets are often indexed by time, space, or both requiring …
world systems. These data sets are often indexed by time, space, or both requiring …
Clustering-based anomaly detection in multivariate time series data
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 …
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
The trilemma among CO2 emissions, energy use, and economic growth in Russia
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 …
Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses …
[PDF][PDF] Comparing time-series clustering algorithms in r using the dtwclust package
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 …
common improvements are either related to the distance measure used to assess …
[HTML][HTML] Multimodal spatiotemporal phenotyping of human retinal organoid development
Organoids generated from human pluripotent stem cells provide experimental systems to
study development and disease, but quantitative measurements across different spatial …
study development and disease, but quantitative measurements across different spatial …
Social media's impact on the consumer mindset: When to use which sentiment extraction tool?
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 …
insights are hindered by a lack of consensus on how to extract brand-relevant valence and …
A classification of public transit users with smart card data based on time series distance metrics and a hierarchical clustering method
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 …
public transit demand analysis. It provides an understanding of people's sequence of …
Time series classification: Nearest neighbor versus deep learning models
W Jiang - SN Applied Sciences, 2020 - Springer
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 …
domains, time series show different patterns, which makes it difficult to design a global …
GTAD: Graph and temporal neural network for multivariate time series anomaly detection
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 …
production equipment, has resulted in a large number of multivariate time series that can be …
[HTML][HTML] Tssearch: Time series subsequence search library
Subsequence search and distance measures are crucial tools in time series data mining.
This paper presents our Python package entitled TSSEARCH, which provides a …
This paper presents our Python package entitled TSSEARCH, which provides a …