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[HTML][HTML] An overview of clustering methods with guidelines for application in mental health research
Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …
individual heterogeneity by identifying more homogeneous subgroups of individuals …
Complex network approaches to nonlinear time series analysis
In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …
complex network methods for the characterization of dynamical systems based on time …
Temporal data meets LLM--explainable financial time series forecasting
This paper presents a novel study on harnessing Large Language Models'(LLMs)
outstanding knowledge and reasoning abilities for explainable financial time series …
outstanding knowledge and reasoning abilities for explainable financial time series …
Tslearn, a machine learning toolkit for time series data
R Tavenard, J Faouzi, G Vandewiele, F Divo… - Journal of machine …, 2020 - jmlr.org
tslearn is a general-purpose Python machine learning library for time series that offers tools
for pre-processing and feature extraction as well as dedicated models for clustering …
for pre-processing and feature extraction as well as dedicated models for clustering …
Deep learning for time series classification
HI Fawaz - arxiv preprint arxiv:2010.00567, 2020 - arxiv.org
Time series analysis is a field of data science which is interested in analyzing sequences of
numerical values ordered in time. Time series are particularly interesting because they allow …
numerical values ordered in time. Time series are particularly interesting because they allow …
A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
AE Ezugwu, AM Ikotun, OO Oyelade… - … Applications of Artificial …, 2022 - Elsevier
Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …
active research in many fields of study, such as computer science, data science, statistics …
The zwicky transient facility: science objectives
Abstract The Zwicky Transient Facility (ZTF), a public–private enterprise, is a new time-
domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope …
domain survey employing a dedicated camera on the Palomar 48-inch Schmidt telescope …
Big Data and cloud computing: innovation opportunities and challenges
Big Data has emerged in the past few years as a new paradigm providing abundant data
and opportunities to improve and/or enable research and decision-support applications with …
and opportunities to improve and/or enable research and decision-support applications with …
Big data preprocessing: methods and prospects
S García, S Ramírez-Gallego, J Luengo, JM Benítez… - Big data analytics, 2016 - Springer
The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …
The trilemma among CO2 emissions, energy use, and economic growth in Russia
C Magazzino, M Mele, C Drago, S Kuşkaya, C Pozzi… - Scientific Reports, 2023 - nature.com
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 …