Unsupervised Anomaly Detection for Intermittent Sequences Based on Multi-Granularity Abnormal Pattern Mining

In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequenc...

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Detalles Bibliográficos
Autores principales: Fan, Lilin, Zhang, Jiahu, Mao, Wentao, Cao, Fukang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857523/
https://www.ncbi.nlm.nih.gov/pubmed/36673264
http://dx.doi.org/10.3390/e25010123

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857523/
https://www.ncbi.nlm.nih.gov/pubmed/36673264
http://dx.doi.org/10.3390/e25010123

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