{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T22:36:16Z","timestamp":1761172576890,"version":"build-2065373602"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,9]]},"DOI":"10.1109\/etfa65518.2025.11205803","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T17:07:47Z","timestamp":1761066467000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Advanced Anomaly Detection Models for Time Series Data of Refrigeration Systems"],"prefix":"10.1109","author":[{"given":"Melina","family":"Meyer","sequence":"first","affiliation":[{"name":"RheinMain Univ. of Applied Sciences,Wiesbaden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julian","family":"Eversheim","sequence":"additional","affiliation":[{"name":"RheinMain Univ. of Applied Sciences,Wiesbaden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Pitterle","sequence":"additional","affiliation":[{"name":"RheinMain Univ. of Applied Sciences,Wiesbaden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Gergeleit","sequence":"additional","affiliation":[{"name":"RheinMain Univ. of Applied Sciences,Wiesbaden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dirk","family":"Krechel","sequence":"additional","affiliation":[{"name":"RheinMain Univ. of Applied Sciences,Wiesbaden,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","volume-title":"International Conference on Learning Representations","author":"Xu","key":"ref1"},{"key":"ref2","first-page":"27 268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"International conference on machine learning","author":"Zhou"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"article-title":"Efficiently modeling long sequences with structured state spaces","volume-title":"The International Conference on Learning Representations (ICLR)","author":"Gu","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11065-0"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCE52056.2021.9514086"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrefrig.2022.08.008"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.applthermaleng.2019.114506"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2018.8431901"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2016.07.014"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IECON43393.2020.9254485"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.115877"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrefrig.2022.12.019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2021.753732"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.10.008"},{"key":"ref16","first-page":"6778","article-title":"Transformers in time series: A survey","volume-title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China","author":"Wen"},{"article-title":"Deep time series models: A comprehensive survey and benchmark","year":"2024","author":"Wang","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"ref19","first-page":"22 419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Advances in neural information processing systems"},{"article-title":"TimesNet: Temporal 2d-variation modeling for general time series analysis","volume-title":"International Conference on Learning Representations","author":"Wu","key":"ref20"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA54631.2023.10275472"},{"key":"ref22","first-page":"1359","article-title":"Anomaly detection in supermarket refrigeration systems using transformer models: A comparative study","volume-title":"INFORMATIK 2024","author":"Meyer","year":"2024"},{"article-title":"Virtus Caelum","year":"2024","author":"AG","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"ref25","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics","volume":"5","author":"MacQueen"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3270293"},{"issue":"289","key":"ref27","first-page":"1","article-title":"aeon: a python toolkit for learning from time series","volume-title":"Journal of Machine Learning Research","volume":"25","author":"Middlehurst","year":"2024"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"article-title":"Reformer: The efficient transformer","year":"2020","author":"Kitaev","key":"ref29"},{"article-title":"Mamba: Linear-time sequence modeling with selective state spaces","year":"2023","author":"Gu","key":"ref30"},{"article-title":"Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality","volume-title":"International Conference on Machine Learning (ICML)","author":"Dao","key":"ref31"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"The eleventh international conference on learning representations","author":"Zhang","key":"ref33"},{"article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","volume-title":"International Conference on Learning Representations","author":"Liu","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"article-title":"itransformer: Inverted transformers are effective for time series forecasting","year":"2023","author":"Liu","key":"ref36"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.21105\/joss.03021"}],"event":{"name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","start":{"date-parts":[[2025,9,9]]},"location":"Porto, Portugal","end":{"date-parts":[[2025,9,12]]}},"container-title":["2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11205475\/11205526\/11205803.pdf?arnumber=11205803","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:17:58Z","timestamp":1761110278000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11205803\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/etfa65518.2025.11205803","relation":{},"subject":[],"published":{"date-parts":[[2025,9,9]]}}}