SCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environments
Metadatos
Afficher la notice complèteEditorial
Springer
Materia
Time series Time series features Feature-based approach Big Data Scalability
Date
2021-11-02Referencia bibliográfica
Baldán, F.J... [et al.]. SCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environments. Int J Comput Intell Syst 14, 186 (2021). [https://doi.org/10.1007/s44196-021-00036-7]
Patrocinador
Spanish Government TIN2016-81113-R BES-2017-080137; Andalusian Regional Government, Spain P12-TIC-2985 P18-TP-5168; European Commission European Commission Joint Research Centre European CommissionRésumé
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.