TY - GEN
T1 - Monitoring Time Series with Missing Values
T2 - 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
AU - Barazani, Oshri
AU - Tolpin, David
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85134178773&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-07689-3_2
DO - 10.1007/978-3-031-07689-3_2
M3 - Conference contribution
AN - SCOPUS:85134178773
SN - 9783031076886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 28
BT - Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
A2 - Dolev, Shlomi
A2 - Meisels, Amnon
A2 - Katz, Jonathan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 June 2022 through 1 July 2022
ER -