{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:53:01Z","timestamp":1774047181340,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030637095","type":"print"},{"value":"9783030637101","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63710-1_13","type":"book-chapter","created":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T00:04:47Z","timestamp":1605485087000},"page":"161-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Time Series Encodings with Temporal Convolutional Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6429-180X","authenticated-orcid":false,"given":"Markus","family":"Thill","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1343-4209","authenticated-orcid":false,"given":"Wolfgang","family":"Konen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-1478","authenticated-orcid":false,"given":"Thomas","family":"B\u00e4ck","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,16]]},"reference":[{"key":"13_CR1","unstructured":"Ahmad, S.: Running swarms (2017). http:\/\/nupic.docs.numenta.org\/0.6.0\/guide-swarming.html. Accessed 29 June 2020"},{"issue":"4","key":"13_CR2","doi-asserted-by":"publisher","first-page":"043116","DOI":"10.1063\/1.5019320","volume":"28","author":"G Ansmann","year":"2018","unstructured":"Ansmann, G.: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE. Chaos 28(4), 043116 (2018)","journal-title":"Chaos"},{"key":"13_CR3","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR abs\/1803.01271 (2018)"},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"014008","DOI":"10.1088\/1749-4699\/8\/1\/014008","volume":"8","author":"J Bergstra","year":"2015","unstructured":"Bergstra, J., et al.: Hyperopt: a Python library for model selection and hyperparameter optimization. Comput. Sci. Discov. 8(1), 014008 (2015)","journal-title":"Comput. Sci. Discov."},{"key":"13_CR5","first-page":"1","volume":"131","author":"DM Chan","year":"2019","unstructured":"Chan, D.M., Rao, R., Huang, F., Canny, J.F.: GPU accelerated T-distributed stochastic neighbor embedding. JPDC 131, 1\u201313 (2019)","journal-title":"JPDC"},{"key":"13_CR6","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: ICML 2017, p. 933\u2013941 (2017)"},{"key":"13_CR7","unstructured":"Fischer, M., et al.: Anomaly Detection on Time Series: An Evaluation of Deep Learning Methods (2019). https:\/\/github.com\/KDD-OpenSource\/DeepADoTS"},{"key":"13_CR8","unstructured":"Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. CoRR abs\/1705.03122 (2017)"},{"issue":"10","key":"13_CR9","doi-asserted-by":"publisher","first-page":"e1000532","DOI":"10.1371\/journal.pcbi.1000532","volume":"5","author":"D George","year":"2009","unstructured":"George, D., Hawkins, J.: Towards a mathematical theory of cortical micro-circuits. PLoS Comput. Biol. 5(10), e1000532 (2009)","journal-title":"PLoS Comput. Biol."},{"issue":"23","key":"13_CR10","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","journal-title":"Circulation"},{"key":"13_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/3-540-46145-0_17","volume-title":"Data Warehousing and Knowledge Discovery","author":"S Hawkins","year":"2002","unstructured":"Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170\u2013180. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-46145-0_17"},{"issue":"8","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"143608","DOI":"10.1109\/ACCESS.2019.2944689","volume":"7","author":"W Jiang","year":"2019","unstructured":"Jiang, W., Hong, Y., Zhou, B., He, X.: A GAN-based anomaly detection approach for imbalanced industrial time series. IEEE Access 7, 143608\u2013143619 (2019)","journal-title":"IEEE Access"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A Convolutional neural network for modelling sentences. In: ACL, Baltimore, Maryland, pp. 655\u2013665 (2014)","DOI":"10.3115\/v1\/P14-1062"},{"key":"13_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"13_CR16","unstructured":"Laptev, N., Amizadeh, S.: Yahoo anomaly detection dataset S5 (2015). http:\/\/webscope.sandbox.yahoo.com\/catalog.php?datatype=s&did=70"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Lavin, A., Ahmad, S.: Evaluating real-time anomaly detection algorithms - the Numenta anomaly benchmark. In: ICMLA (2015)","DOI":"10.1109\/ICMLA.2015.141"},{"key":"13_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-030-30490-4_56","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series","author":"D Li","year":"2019","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703\u2013716. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30490-4_56"},{"key":"13_CR19","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)"},{"key":"13_CR20","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"4300","key":"13_CR21","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1126\/science.267326","volume":"197","author":"MC Mackey","year":"1977","unstructured":"Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287\u2013289 (1977)","journal-title":"Science"},{"key":"13_CR22","unstructured":"Malhotra, P., et al.: LSTM-based encoder-decoder for multi-sensor anomaly detection. CoRR abs\/1607.00148 (2016)"},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1109\/ACCESS.2018.2886457","volume":"7","author":"M Munir","year":"2019","unstructured":"Munir, M., et al.: DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991\u20132005 (2019)","journal-title":"IEEE Access"},{"key":"13_CR24","unstructured":"van den Oord, A., et al.: WaveNet: a generative model for raw audio. CoRR abs\/1609.03499 (2016)"},{"key":"13_CR25","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., et al. (eds.) NIPS, pp. 8024\u20138035. Curran Assoc. (2019)"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Pereira, J., Silveira, M.: Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. In: Wani, M.A., et al. (eds.) ICMLA, pp. 1275\u20131282. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00207"},{"key":"13_CR27","unstructured":"S\u00f6lch, M., et al.: Variational inference for on-line anomaly detection in high-dimensional time series. CoRR abs\/1602.07109 (2016)"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Taylor, M., et al.: numenta\/nupic: 1.0.5 (2018). https:\/\/doi.org\/10.5281\/zenodo.1257382","DOI":"10.5281\/zenodo.1257382"},{"key":"13_CR29","unstructured":"Thill, M., D\u00e4ubener, S., Konen, W., B\u00e4ck, T.: Anomaly detection in electrocardiogram readings with stacked LSTM networks. In: ITAT. CEUR Workshop Proceedings, vol. 2473, pp. 17\u201325 (2019)"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Thill, M., Konen, W., B\u00e4ck, T.: Online anomaly detection on the Webscope S5 dataset: a comparative study. In: EAIS, pp. 1\u20138. IEEE (2017)","DOI":"10.1109\/EAIS.2017.7954844"},{"key":"13_CR31","doi-asserted-by":"publisher","unstructured":"Thill, M., Konen, W., B\u00e4ck, T.: MGAB: The Mackey-Glass Anomaly Benchmark (2020). https:\/\/doi.org\/10.5281\/zenodo.3762385","DOI":"10.5281\/zenodo.3762385"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. In: WWW, pp. 187\u2013196 (2018)","DOI":"10.1145\/3178876.3185996"},{"key":"13_CR33","unstructured":"Zong, B., et al.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: ICLR (2018)"}],"container-title":["Lecture Notes in Computer Science","Bioinspired Optimization Methods and Their Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63710-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T00:07:36Z","timestamp":1605485256000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-63710-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030637095","9783030637101"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63710-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"16 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIOMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bioinspired Methods and Their Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brussels","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bioma2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/utopiae.eu\/bioma-2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic BIOMA 2020 was held as a virtual event.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}