{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:47:27Z","timestamp":1782406047796,"version":"3.54.5"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031264214","type":"print"},{"value":"9783031264221","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-26422-1_1","type":"book-chapter","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:08:37Z","timestamp":1679879317000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Few-Shot Forecasting of\u00a0Time-Series with\u00a0Heterogeneous Channels"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5754-1746","authenticated-orcid":false,"given":"Lukas","family":"Brinkmeyer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6607-3208","authenticated-orcid":false,"given":"Rafael Rego","family":"Drumond","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Burchert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5729-6023","authenticated-orcid":false,"given":"Lars","family":"Schmidt-Thieme","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-030-91445-5_8","volume-title":"Advanced Analytics and Learning on Temporal Data","author":"SP Arango","year":"2021","unstructured":"Arango, S.P., Heinrich, F., Madhusudhanan, K., Schmidt-Thieme, L.: Multimodal meta-learning for time series regression. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds.) AALTD 2021. LNCS (LNAI), vol. 13114, pp. 123\u2013138. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91445-5_8"},{"key":"1_CR2","unstructured":"Bagnall, A., et al.: The UEA multivariate time series classification archive (2018). arXiv preprint arXiv:1811.00075 (2018)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Box, G.E.P., Jenkins, G.M.: Some recent advances in forecasting and control. J. R. Stat. Soc. Ser. C (Applied Statistics) 17(2), 91\u2013109 (1968). http:\/\/www.jstor.org\/stable\/2985674","DOI":"10.2307\/2985674"},{"key":"1_CR4","unstructured":"Brinkmeyer, L., Drumond, R.R., Scholz, R., Grabocka, J., Schmidt-Thieme, L.: Chameleon: learning model initializations across tasks with different schemas. arXiv preprint arXiv:1909.13576 (2019)"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"1_CR6","unstructured":"Dai, Z., Liu, H., Le, Q., Tan, M.: Coatnet: marrying convolution and attention for all data sizes. Advances in Neural IHou, R., Chang, H., Ma, B., Shan, S., and Chen, X. (2019). Cross attention network for few-shot classification. Advances in Neural Information Processing Systems, 32. Information Processing Systems 34 (2021)"},{"issue":"6","key":"1_CR7","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1109\/JAS.2019.1911747","volume":"6","author":"HA Dau","year":"2019","unstructured":"Dau, H.A., et al.: The UCR time series archive. IEEE\/CAA J. Automatica Sinica 6(6), 1293\u20131305 (2019)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Drumond, R.R., Brinkmeyer, L., Grabocka, J., Schmidt-Thieme, L.: Hidra: Head initialization across dynamic targets for robust architectures. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 397\u2013405. SIAM (2020). https:\/\/epubs.siam.org\/doi\/abs\/10.1137\/1.9781611976236.45","DOI":"10.1137\/1.9781611976236.45"},{"key":"1_CR10","unstructured":"Drumond, R.R., Marques, B.A., Vasconcelos, C.N., Clua, E.: Peek-an lstm recurrent network for motion classification from sparse data. In: VISIGRAPP (1: GRAPP), pp. 215\u2013222 (2018)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Feurer, M., Springenberg, J., Hutter, F.: Initializing bayesian hyperparameter optimization via meta-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)","DOI":"10.1609\/aaai.v29i1.9354"},{"key":"1_CR12","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR (2017)"},{"key":"1_CR13","unstructured":"Foret, P., Kleiner, A., Mobahi, H., Neyshabur, B.: Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:2010.01412 (2020)"},{"key":"1_CR14","unstructured":"Godahewa, R., Bergmeir, C., Webb, G.I., Hyndman, R.J., Montero-Manso, P.: Monash time series forecasting archive. arXiv preprint arXiv:2105.06643 (2021)"},{"key":"1_CR15","unstructured":"Gupta, A., Mendonca, R., Liu, Y., Abbeel, P., Levine, S.: Meta-reinforcement learning of structured exploration strategies. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"1_CR16","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2(7) (2015)"},{"key":"1_CR17","unstructured":"Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"1_CR18","unstructured":"Iwata, T., Kumagai, A.: Few-shot learning for time-series forecasting. arXiv preprint arXiv:2009.14379 (2020)"},{"key":"1_CR19","first-page":"6053","volume":"33","author":"T Iwata","year":"2020","unstructured":"Iwata, T., Kumagai, A.: Meta-learning from tasks with heterogeneous attribute spaces. Adv. Neural Inf. Process. Syst. 33, 6053\u20136063 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/978-3-030-86486-6_30","volume-title":"Machine Learning and Knowledge Discovery in Databases. Research Track","author":"S Jawed","year":"2021","unstructured":"Jawed, S., Jomaa, H., Schmidt-Thieme, L., Grabocka, J.: Multi-task learning curve forecasting across hyperparameter configurations and datasets. In: Oliver, N., P\u00e9rez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds.) ECML PKDD 2021. LNCS (LNAI), vol. 12975, pp. 485\u2013501. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86486-6_30"},{"key":"1_CR21","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"1_CR22","unstructured":"Kitaev, N., Kaiser, \u0141., Levskaya, A.: Reformer: the efficient transformer. arXiv preprint arXiv:2001.04451 (2020)"},{"key":"1_CR23","unstructured":"Krollner, B., Vanstone, B.J., Finnie, G.R., et al.: Financial time series forecasting with machine learning techniques: a survey. In: ESANN (2010)"},{"issue":"2194","key":"1_CR24","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim, B., Zohren, S.: Time-series forecasting with deep learning: a survey. Philos. Trans. R. Society A 379(2194), 20200209 (2021)","journal-title":"Philos. Trans. R. Society A"},{"key":"1_CR25","unstructured":"Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)"},{"key":"1_CR26","unstructured":"Liu, M., Zeng, A., Xu, Z., Lai, Q., Xu, Q.: Time series is a special sequence: forecasting with sample convolution and interaction. arXiv preprint arXiv:2106.09305 (2021)"},{"key":"1_CR27","unstructured":"Madhusudhanan, K., Burchert, J., Duong-Trung, N., Born, S., Schmidt-Thieme, L.: Yformer: u-net inspired transformer architecture for far horizon time series forecasting. arXiv preprint arXiv:2110.08255 (2021)"},{"issue":"4","key":"1_CR28","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.ijforecast.2018.06.001","volume":"34","author":"S Makridakis","year":"2018","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: results, findings, conclusion and way forward. Int. J. Forecast. 34(4), 802\u2013808 (2018)","journal-title":"Int. J. Forecast."},{"issue":"3","key":"1_CR29","doi-asserted-by":"publisher","first-page":"e0194889","DOI":"10.1371\/journal.pone.0194889","volume":"13","author":"S Makridakis","year":"2018","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: concerns and ways forward. PLoS ONE 13(3), e0194889 (2018)","journal-title":"PLoS ONE"},{"key":"1_CR30","unstructured":"Maron, H., Ben-Hamu, H., Shamir, N., Lipman, Y.: Invariant and equivariant graph networks. arXiv preprint arXiv:1812.09902 (2018)"},{"key":"1_CR31","unstructured":"Munkhdalai, T., Yu, H.: Meta networks. In: International Conference on Machine Learning, pp. 2554\u20132563. PMLR (2017)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Narwariya, J., Malhotra, P., Vig, L., Shroff, G., Vishnu, T.: Meta-learning for few-shot time series classification. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp. 28\u201336 (2020)","DOI":"10.1145\/3371158.3371162"},{"key":"1_CR33","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)"},{"key":"1_CR34","unstructured":"Oh, J., Yoo, H., Kim, C., Yun, S.Y.: BOIL: towards representation change for few-shot learning. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=umIdUL8rMH"},{"key":"1_CR35","unstructured":"Oliveira, E.M.: Quality prediction in a mining process. https:\/\/www.kaggle.com\/datasets\/shasun\/tool-wear-detection-in-cnc-mill?select=README.txt"},{"key":"1_CR36","doi-asserted-by":"crossref","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: Meta-learning framework with applications to zero-shot time-series forecasting. arXiv preprint arXiv:2002.02887 (2020)","DOI":"10.1609\/aaai.v35i10.17115"},{"key":"1_CR37","unstructured":"Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: Neural basis expansion analysis for interpretable time series forecasting. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=r1ecqn4YwB"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.: A dual-stage attention-based recurrent neural network for time series prediction. arXiv preprint arXiv:1704.02971 (2017)","DOI":"10.24963\/ijcai.2017\/366"},{"key":"1_CR39","unstructured":"Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T.: Deep state space models for time series forecasting. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018). https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/5cf68969fb67aa6082363a6d4e6468e2-Paper.pdf"},{"key":"1_CR40","unstructured":"Rusu, A.A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., Hadsell, R.: Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960 (2018)"},{"issue":"3","key":"1_CR41","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: Deepar: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181\u20131191 (2020)","journal-title":"Int. J. Forecast."},{"key":"1_CR42","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842\u20131850. PMLR (2016)"},{"issue":"1","key":"1_CR43","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.ijforecast.2019.03.017","volume":"36","author":"S Smyl","year":"2020","unstructured":"Smyl, S.: A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. Int. J. Forecast. 36(1), 75\u201385 (2020)","journal-title":"Int. J. Forecast."},{"key":"1_CR44","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1_CR45","unstructured":"Sun, S.: Cnc mill tool wear. https:\/\/www.kaggle.com\/datasets\/shasun\/tool-wear-detection-in-cnc-mill?select=README.txt"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Tang, W., Liu, L., Long, G.: Interpretable time-series classification on few-shot samples. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206860"},{"key":"1_CR47","unstructured":"Tolstikhin, I.O., et al.: Mlp-mixer: an all-mlp architecture for vision. In: Advances in Neural Information Processing Systems (2021)"},{"key":"1_CR48","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"1_CR49","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"1_CR50","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.procir.2018.03.150","volume":"72","author":"A Von Birgelen","year":"2018","unstructured":"Von Birgelen, A., Buratti, D., Mager, J., Niggemann, O.: Self-organizing maps for anomaly localization and predictive maintenance in cyber-physical production systems. Procedia cirp 72, 480\u2013485 (2018)","journal-title":"Procedia cirp"},{"issue":"3","key":"1_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"1_CR52","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International joint conference on neural networks (IJCNN), pp. 1578\u20131585. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"1_CR53","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of AAAI (2021)","DOI":"10.1609\/aaai.v35i12.17325"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26422-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T19:09:41Z","timestamp":1683054581000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26422-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264214","9783031264221"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26422-1_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"22% - 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":"3-4","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","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)"}}]}}