Leveraging Time Series Autocorrelation Through Numerical Differentiation for Improving Failure Prediction-Reference-Cited by-同舟云学术

Leveraging Time Series Autocorrelation Through Numerical Differentiation for Improving Failure Prediction

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:12th Latin-American Symposium on Dependable and Secure Computing
language:
Short-container-title:

Author:

Campos João R.¹^ORCID,Machado Rodrigo¹^ORCID,Vieira Marco¹^ORCID

Affiliation:

1. University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Portugal

Funder

Recovery and Resilience Plan (PRR)

Foundation for Science and Technology

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615366.3615423

Reference46 articles.

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4. Gianluca Bontempi , Souhaib Ben Taieb, and Yann Aël Le Borgne . 2013 . Machine learning strategies for time series forecasting. Lecture Notes in Business Information Processing 138 LNBIP ( 2013), 62–77. https://doi.org/10.1007/978-3-642-36318-4_3 arxiv:z0037 10.1007/978-3-642-36318-4_3 Gianluca Bontempi, Souhaib Ben Taieb, and Yann Aël Le Borgne. 2013. Machine learning strategies for time series forecasting. Lecture Notes in Business Information Processing 138 LNBIP (2013), 62–77. https://doi.org/10.1007/978-3-642-36318-4_3 arxiv:z0037

5. Ben Brown. 2023. Facebook’s Catastrophic Blackout Could Cost $90 Million in Lost Revenue. https://www.ccn.com/facebooks-blackout-90-million-lost-revenue/ Accessed 2023-05-24. Ben Brown. 2023. Facebook’s Catastrophic Blackout Could Cost $90 Million in Lost Revenue. https://www.ccn.com/facebooks-blackout-90-million-lost-revenue/ Accessed 2023-05-24.

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