@inproceedings{15998739f1914f66a1a63f8df7ca32d7,
title = "Data Streams Are Time Series: Challenging Assumptions",
abstract = "The increasingly relevance of data streams in the context of machine learning and artificial intelligence has motivated this paper which discusses and draws necessary relationships between the concepts of data streams and time series in attempt to build on theoretical foundations to support online learning in such scenarios. We unify the concepts of data streams and time series by assessing their definitions in the literature and discuss the major implications of this claim on the way that data streams research and practice is carried out, showing that many common assumptions are incorrect or unnecessary. We analyzed six data sources typically used in benchmark data-stream classification and found that none of those meet the requirements and assumptions qualifying them for online learning.",
keywords = "Data streams, Statistical Learning Theory, Time series",
author = "Jesse Read and Rios, \{Ricardo A.\} and Tatiane Nogueira and \{de Mello\}, \{Rodrigo F.\}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 9th Brazilian Conference on Intelligent Systems, BRACIS 2020 ; Conference date: 20-10-2020 Through 23-10-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-61380-8\_36",
language = "English",
isbn = "9783030613792",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "529--543",
editor = "Ricardo Cerri and Prati, \{Ronaldo C.\}",
booktitle = "Intelligent Systems - 9th Brazilian Conference, BRACIS 2020, Proceedings",
}