@misc{10481/74479,
year = {2021},
month = {11},
url = {http://hdl.handle.net/10481/74479},
abstract = {Time series data are becoming increasingly important due to the interconnectedness of the world. Classical problems, which
are getting bigger and bigger, require more and more resources for their processing, and Big Data technologies offer many
solutions. Although the principal algorithms for traditional vector-based problems are available in Big Data environments,
the lack of tools for time series processing in these environments needs to be addressed. In this work, we propose a scalable
and distributed time series transformation for Big Data environments based on well-known time series features (SCMFTS),
which allows practitioners to apply traditional vector-based algorithms to time series problems. The proposed transformation,
along with the algorithms available in Spark, improved the best results in the state-of-the-art on the Wearable Stress
and Affect Detection dataset, which is the biggest publicly available multivariate time series dataset in the University of
California Irvine (UCI) Machine Learning Repository. In addition, SCMFTS showed a linear relationship between its runtime
and the number of processed time series, demonstrating a linear scalable behavior, which is mandatory in Big Data
environments. SCMFTS has been implemented in the Scala programming language for the Apache Spark framework, and
the code is publicly available.},
organization = {Spanish Government	TIN2016-81113-R	
BES-2017-080137},
organization = {Andalusian Regional Government, Spain	P12-TIC-2985	
P18-TP-5168},
organization = {European Commission
European Commission Joint Research Centre		
European Commission},
publisher = {Springer},
keywords = {Time series},
keywords = {Time series features},
keywords = {Feature-based approach},
keywords = {Big Data},
keywords = {Scalability},
title = {SCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environments},
doi = {10.1007/s44196-021-00036-7},
author = {Baldán Lozano, Francisco Javier and Benítez Sánchez, José Manuel},
}