Towards time-evolving analytics: Online learning for time-dependent evolving data streams

Robert Hoehndorf, Giacomo Ziffer*, Alessio Bernardo, Emanuele Della Valle, Vitor Cerqueira, Albert Bifet

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalData Science
Issue number1-2
StatePublished - Dec 8 2023

Bibliographical note

Publisher Copyright:
© 2023 - The authors. Published by IOS Press.


  • concept drift
  • Streaming Machine Learning
  • temporal dependence
  • Time Evolving Analytics
  • Time Series Analysis

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Statistics and Probability
  • Information Systems
  • Modeling and Simulation
  • Computational Mathematics
  • Artificial Intelligence


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