Monitoring Time Series with Missing Values: A Deep Probabilistic Approach

Oshri Barazani, David Tolpin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible to forecast in high-dimensional time series, and identify and classify novelties early, based on subtle changes in the trends. However, mainstream approaches to multi-variate time series predictions do not handle well cases when the ongoing forecasts must include uncertainty, nor they are robust to missing data. We introduce a new architecture for time series monitoring based on combination of state-of-the-art methods of forecasting in high-dimensional time series with full probabilistic handling of uncertainty. We demonstrate advantage of the architecture for time series forecasting and novelty detection, in particular with partially missing data, and empirically evaluate and compare the architecture to state-of-the-art approaches on a real-world data set.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
EditorsShlomi Dolev, Amnon Meisels, Jonathan Katz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031076886
StatePublished - 1 Jan 2022
Event6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel
Duration: 30 Jun 20221 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13301 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
CityBeer Sheva

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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