{"schema":"pith.reference-change-event.v1","doi":"10.1007/s11831-021-09616-4","canonical_url":"https://pith.science/event/10.1007/s11831-021-09616-4","json_url":"https://pith.science/event/10.1007/s11831-021-09616-4.json","not_a_judgment":"This page records that a citing paper's bibliography includes a work with a published notice. It is not a judgment on the citing paper.","primary":{"event_id":322166,"doi":"10.1007/s11831-021-09616-4","event_type":"correction","event_type_label":"Correction","source":"crossref","source_label":"Crossref","event_date":"2021-07-20","title":"Correction to: Big Data Analytics in Weather Forecasting: A Systematic Review","work_title":null,"work_doi":"10.1007/s11831-021-09616-4","work_arxiv_id":null,"notice_doi":"10.1007/s11831-021-09630-6","flag_count":0,"flags_open":0,"flags_disputed":0,"latest_flag_at":null,"human_href":"/event/10.1007/s11831-021-09616-4","json_href":"/event/10.1007/s11831-021-09616-4.json"},"events":[{"event_id":322166,"doi":"10.1007/s11831-021-09616-4","event_type":"correction","event_type_label":"Correction","source":"crossref","source_label":"Crossref","event_date":"2021-07-20","title":"Correction to: Big Data Analytics in Weather Forecasting: A Systematic Review","work_title":null,"work_doi":"10.1007/s11831-021-09616-4","work_arxiv_id":null,"notice_doi":"10.1007/s11831-021-09630-6","flag_count":0,"flags_open":0,"flags_disputed":0,"latest_flag_at":null,"human_href":"/event/10.1007/s11831-021-09616-4","json_href":"/event/10.1007/s11831-021-09616-4.json"}],"flags":[{"id":8686,"status":"open","status_label":"Open","citing_arxiv_id":"2507.23615","citing_title":"L-GTA: Latent Generative Modeling for Time Series Augmentation","ref_index":5,"evidence_raw":"[4] Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, and Y. Bengio. 2015. A Recurrent Latent Variable Model for Sequential Data. 8. [5] Marzieh Fathi, Mostafa Haghi Kashani, Seyed Mahdi Jameii, and Ebrahim Mahdipour. 2022. Big Data Analytics in Weather Forecasting: A Systematic Review. Archives of Computational Methods in Engineering 29, 2 (Mar. 2022), 1247-1275. doi:10.1007/s11831-021-09616-4 [6] Simone Gitto, Carmela Di Mauro, Alessandro Ancarani, and Paolo Mancuso. 2021. Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy. Plos one 16, 2 (2021), e0247726. [7] Maxime Goubeaud, Philipp Joußen, Nicolla Gmyrek, Farzin Ghorban, Lucas Schelkes, and Anton Kummert. 2021. Using Variational Autoencoder to augment","evidence_cleaned":null,"evidence_source_label":"citation context","event_type":"correction","event_type_label":"Correction","source_label":"Crossref","event_date":"2021-07-20","work_title":null,"work_doi":"10.1007/s11831-021-09616-4","event_doi":"10.1007/s11831-021-09616-4","flag_href":"/flags/8686","event_href":"/event/10.1007/s11831-021-09616-4","paper_href":"/paper/2507.23615","created_at":"2026-08-06T12:17:46.120080Z","dispute_note":null,"disputed_at":null,"disputed_by":null}],"flag_count":1,"flags_open":1,"flags_disputed":0,"desk_url":"https://pith.science/flags"}