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Open Access

Peer-reviewed

Research Article

Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

Ali Caner Türkmen ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: atturkm@amazon.com

¶‡ Work done before joining Amazon.

Affiliation Amazon Web Services AI Labs, Berlin, Germany

https://orcid.org/0000-0003-2593-1824

⨯
Tim Januschowski,

Roles Conceptualization, Formal analysis, Investigation, Project administration, Supervision, Writing – review & editing

Affiliation Amazon Web Services AI Labs, Berlin, Germany
⨯
Yuyang Wang,

Roles Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Amazon Web Services AI Labs, East Palo Alto, CA, United States of America
⨯
Ali Taylan Cemgil

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliation Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey
⨯

Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

Ali Caner Türkmen,
Tim Januschowski,
Yuyang Wang,
Ali Taylan Cemgil

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Published: November 29, 2021
https://doi.org/10.1371/journal.pone.0259764

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