{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:22:27Z","timestamp":1781688147421,"version":"3.54.5"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031756221","type":"print"},{"value":"9783031756238","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-75623-8_35","type":"book-chapter","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T19:17:00Z","timestamp":1735845420000},"page":"456-471","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Auto-sktime: Automated Time Series Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8705-9862","authenticated-orcid":false,"given":"Marc-Andr\u00e9","family":"Z\u00f6ller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9675-3175","authenticated-orcid":false,"given":"Marius","family":"Lindauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8250-2092","authenticated-orcid":false,"given":"Marco F.","family":"Huber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"35_CR1","unstructured":"Carme, A.: Time Series Datasets (2023)"},{"key":"35_CR2","unstructured":"Bauer, M., van Dinther, C., Kiefer, D.: Machine learning in SME: an empirical study on enablers and success factors success factors. In: Americas\u2019 Conference on Information Systems, pp. 1\u201310 (2020)"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Bischl, B., et al.: Hyperparameter optimization: foundations, algorithms, best practices, and open challenges. WIRE 13(2) (2023)","DOI":"10.1002\/widm.1484"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Bontempi, G., Ben Taieb, S., Le Borgne, Y.A.: Machine learning strategies for time series forecasting. In: LNBIP, vol. 138, pp. 62\u201377 (2013)","DOI":"10.1007\/978-3-642-36318-4_3"},{"key":"35_CR5","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control, 5 edn. Wiley (2015)"},{"key":"35_CR6","unstructured":"Brochu, E., Cora, V.M., de\u00a0Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, pp. 1\u201349 (2010). arXiv:1012.2599"},{"issue":"9","key":"35_CR7","doi-asserted-by":"publisher","first-page":"2969","DOI":"10.1007\/s00500-018-3597-8","volume":"23","author":"A Candelieri","year":"2019","unstructured":"Candelieri, A., Archetti, F.: Global optimization in machine learning: the design of a predictive analytics application. Soft. Comput. 23(9), 2969\u20132977 (2019)","journal-title":"Soft. Comput."},{"key":"35_CR8","unstructured":"Carme, A.: PyAF: Python Automatic Forecasting (2016)"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: International Conference on Machine Learning, p.\u00a018 (2004)","DOI":"10.1145\/1015330.1015432"},{"key":"35_CR10","unstructured":"Catlin, C.: AutoTS (2020)"},{"issue":"1","key":"35_CR11","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1080\/24709360.2017.1396742","volume":"1","author":"YC Chen","year":"2017","unstructured":"Chen, Y.C.: A tutorial on kernel density estimation and recent advances. Biostat. Epidemiol. 1(1), 161\u2013187 (2017)","journal-title":"Biostat. Epidemiol."},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP, pp. 1724\u20131734 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"35_CR13","unstructured":"Dahl, S.M.J.: TSPO: An AutoML Approach to Time Series Forecasting (2020)"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Deng, D., Karl, F., Hutter, F., Bischl, B., Lindauer, M.: Efficient automated deep learning for time series forecasting. In: ECML (2022)","DOI":"10.1007\/978-3-031-26409-2_40"},{"key":"35_CR15","unstructured":"Erickson, N., et al.: Robust and accurate AutoML for structured data. In: ICML, pp. 1\u201328(2020)"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Feurer, M., Hutter, F.: Hyperparameter optimization. In: Automatic Machine Learning: Methods, Systems, Challenges, pp. 3\u201338. Springer (2018)","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"35_CR17","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenber, J.T., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: NeurIPS (2015)"},{"issue":"8","key":"35_CR18","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":"35_CR19","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: LION, pp. 507\u2013523 (2011)","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"35_CR20","unstructured":"Hvarfner, C., Stoll, D., Souza, A., Lindauer, M., Hutter, F., Nardi, L.: piBO:Augmenting acquisition functions with user beliefs for BO. In: ICLR, pp. 1\u201315 (2022)"},{"key":"35_CR21","unstructured":"Hyndman, R.J., Athanalos, G.: Forecasting: Principles and Practice. OTexts (2021)"},{"issue":"4","key":"35_CR22","first-page":"1473","volume":"38","author":"T Januschowski","year":"2022","unstructured":"Januschowski, T., Wang, Y., Torkkola, K., Erkkil\u00e4, T., Hasson, H., Gasthaus, J.: Forecasting with trees. IJF 38(4), 1473\u20131481 (2022)","journal-title":"IJF"},{"key":"35_CR23","unstructured":"Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.: Fast BO of ML hyperparameters on large datasets. In: AISTATS, pp. 528\u2013536 (2016)"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Kurian, J.J., Dix, M., Amihai, I., Ceusters, G., Prabhune, A.: BOAT: a Bayesian optimization AutoML time-series framework for industrial applications. In: IEEE BigDataService, pp. 17\u201324 (2021)","DOI":"10.1109\/BigDataService52369.2021.00008"},{"issue":"1","key":"35_CR25","first-page":"6765","volume":"18","author":"L Li","year":"2017","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. JMLR 18(1), 6765\u20136816 (2017)","journal-title":"JMLR"},{"key":"35_CR26","unstructured":"Li, Y.L., Rudner, T.G.J., Wilson, A.G.: A study of Bayesian neural network surrogates for Bayesian optimization. In: AABI, pp. 1\u201341 (2023)"},{"issue":"4","key":"35_CR27","first-page":"1748","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim, B., Ar\u0131k, S., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. IJF 37(4), 1748\u20131764 (2021)","journal-title":"IJF"},{"key":"35_CR28","first-page":"1","volume":"23","author":"M Lindauer","year":"2022","unstructured":"Lindauer, M., et al.: SMAC3: a versatile Bayesian optimization package for hyperparameter optimization. JMLR 23, 1\u20139 (2022)","journal-title":"JMLR"},{"key":"35_CR29","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neunet.2021.02.017","volume":"139","author":"PS Maciag","year":"2021","unstructured":"Maciag, P.S., Kryszkiewicz, M., Bembenik, R., Lobo, J.L., Del Ser, J.: Unsupervised anomaly detection in stream data with online evolving spiking neural networks. Neural Netw. 139, 118\u2013139 (2021)","journal-title":"Neural Netw."},{"issue":"1","key":"35_CR30","first-page":"54","volume":"36","author":"S Makridakis","year":"2020","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The M4 competition: 100,000 time series and 61 forecasting methods. IJF 36(1), 54\u201374 (2020)","journal-title":"IJF"},{"issue":"1","key":"35_CR31","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1111\/joes.12429","volume":"37","author":"RP Masini","year":"2023","unstructured":"Masini, R.P., Medeiros, M.C., Mendes, E.F.: Machine learning advances for time series forecasting. J. Econ. Surv. 37(1), 76\u2013111 (2023)","journal-title":"J. Econ. Surv."},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Meisenbacher, S., et al.: Review of automated time series forecasting pipelines. WIRE (2022)","DOI":"10.1002\/widm.1475"},{"issue":"3","key":"35_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3558774","volume":"2","author":"Y Pushak","year":"2022","unstructured":"Pushak, Y., Hoos, H.: AutoML loss landscapes. ACM Trans. Evol. Learn. Optim. 2(3), 1\u201330 (2022)","journal-title":"ACM Trans. Evol. Learn. Optim."},{"issue":"1","key":"35_CR34","doi-asserted-by":"publisher","first-page":"229","DOI":"10.18576\/jsap\/100121","volume":"10","author":"EHA Rady","year":"2021","unstructured":"Rady, E.H.A., Fawzy, H., Fattah, A.M.A.: Time series forecasting using tree based methods. J. Stat. Appl. Probab. 10(1), 229\u2013244 (2021)","journal-title":"J. Stat. Appl. Probab."},{"key":"35_CR35","unstructured":"Rasmussen, C., Williams, C.: Gaussian Processes for ML. MIT Press (2006)"},{"key":"35_CR36","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J., Koehler, A.B., Keith Ord, J., Snyder, R.D.: Forecasting with Exponential Smoothing: The State Space Approach. Springer (2008)","DOI":"10.1007\/978-3-540-71918-2"},{"key":"35_CR37","unstructured":"Mulla, R.: Time Series Datasets (2018)"},{"key":"35_CR38","doi-asserted-by":"crossref","unstructured":"de\u00a0S\u00e1, A., Pinto, W., Oliveira, L., Pappa, G.: RECIPE: a grammar-based framework for automatically evolving classification pipelines. In: EuroGP (2017)","DOI":"10.1007\/978-3-319-55696-3_16"},{"issue":"3","key":"35_CR39","first-page":"1181","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. IJF 36(3), 1181\u20131191 (2020)","journal-title":"IJF"},{"key":"35_CR40","doi-asserted-by":"crossref","unstructured":"Shah, S., et al.: AutoAI-TS: AutoAI for time series forecasting. In: SIGMOD, pp. 2584\u20132596 (2021)","DOI":"10.1145\/3448016.3457557"},{"key":"35_CR41","unstructured":"Shchur, O., et al.: AutoML for probabilistic time series forecasting. In: AutoML Conference (2023)"},{"key":"35_CR42","doi-asserted-by":"crossref","unstructured":"da\u00a0Silva, F., Vieira, A., Bernardino, H., Alencar, V., Pessamilio, L., Barbosa, H.: Automated machine learning for time series prediction. In: IEEE CEC (2022)","DOI":"10.1109\/CEC55065.2022.9870305"},{"key":"35_CR43","doi-asserted-by":"crossref","unstructured":"Smith, M.J., Wedge, R., Veeramachaneni, K.: FeatureHub: towards collaborative data science. In: IEEE DSAA, pp. 590\u2013600 (2017)","DOI":"10.1109\/DSAA.2017.66"},{"key":"35_CR44","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and HPO of classification algorithms. In: ACM KDD, pp. 847\u2013855 (2013)","DOI":"10.1145\/2487575.2487629"},{"key":"35_CR45","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-05318-5_2","volume-title":"Automated Machine Learning","author":"J Vanschoren","year":"2019","unstructured":"Vanschoren, J.: Meta-learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 35\u201361. Springer, Cham (2019)"},{"issue":"1","key":"35_CR46","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/BF01074755","volume":"4","author":"T Vintsyuk","year":"1968","unstructured":"Vintsyuk, T.: Speech discrimination by dynamic programming. Cybernetics 4(1), 52\u201357 (1968)","journal-title":"Cybernetics"},{"key":"35_CR47","doi-asserted-by":"crossref","unstructured":"Vogelsgesang, A., et al.: Get real: how benchmarks fail to represent the real world. In: Workshop on Testing Database Systems (2018)","DOI":"10.1145\/3209950.3209952"},{"key":"35_CR48","doi-asserted-by":"crossref","unstructured":"Yamak, P.T., Yujian, L., Gadosey, P.K.: A comparison between ARIMA, LSTM, and GRU for time series forecasting. In: ACAI, pp. 49\u201355 (2020)","DOI":"10.1145\/3377713.3377722"},{"key":"35_CR49","unstructured":"Zhang, X., Wu, H., Yang, J.: HyperTS: a full-pipeline automated time series analysis toolkit (2022)"},{"key":"35_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: AAAI, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"35_CR51","doi-asserted-by":"crossref","unstructured":"Z\u00f6ller, M.A., Mauthe, F., Zeiler, P., Lindauer, M., Huber, M.F.: Automated machine learning for remaining useful life predictions. In: IEEE SMC (2023)","DOI":"10.1109\/SMC53992.2023.10394031"}],"container-title":["Lecture Notes in Computer Science","Learning and Intelligent Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75623-8_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T09:09:34Z","timestamp":1741684174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75623-8_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031756221","9783031756238"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75623-8_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LION","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Learning and Intelligent Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ischia Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lion2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.lion18.unina.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}