{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:10:19Z","timestamp":1758273019348,"version":"3.40.5"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031735028"},{"type":"electronic","value":"9783031735035"}],"license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"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\/978-3-031-73503-5_16","type":"book-chapter","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T04:01:36Z","timestamp":1731643296000},"page":"188-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Empirical Evaluation of\u00a0DeepAR for\u00a0Univariate Time Series Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1814-071X","authenticated-orcid":false,"given":"Ricardo","family":"Urjais Gomes","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4549-8917","authenticated-orcid":false,"given":"Carlos","family":"Soares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4709-1718","authenticated-orcid":false,"given":"Luis Paulo","family":"Reis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.108325","volume":"213","author":"M Afrasiabi","year":"2022","unstructured":"Afrasiabi, M., Aghaei, J., Afrasiabi, S., Mohammadi, M.: Probability density function forecasting of electricity price: deep Gabor convolutional mixture network. Electric Power Syst. Res. 213, 108325 (2022). https:\/\/doi.org\/10.1016\/j.epsr.2022.108325","journal-title":"Electric Power Syst. Res."},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.ins.2023.01.095","volume":"628","author":"Y Dong","year":"2023","unstructured":"Dong, Y., Xiao, L., Wang, J., Wang, J.: A time series attention mechanism based model for tourism demand forecasting. Inf. Sci. 628, 269\u2013290 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.01.095","journal-title":"Inf. Sci."},{"key":"16_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecmx.2022.100239","volume":"15","author":"OF Eikeland","year":"2022","unstructured":"Eikeland, O.F., Hovem, F.D., Olsen, T.E., Chiesa, M., Bianchi, F.M.: Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An arctic case. Energy Conv. Manag. X 15, 100239 (2022). https:\/\/doi.org\/10.1016\/j.ecmx.2022.100239","journal-title":"Energy Conv. Manag. X"},{"key":"16_CR4","doi-asserted-by":"publisher","unstructured":"Fildes, R., Ma, S., Kolassa, S.: Retail forecasting: research and practice. Int. J. Forecast. 38(4), 1283\u20131318 (2022). https:\/\/doi.org\/10.1016\/j.ijforecast.2019.06.004. Special Issue: M5 competition","DOI":"10.1016\/j.ijforecast.2019.06.004"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s42488-019-00004-z","volume":"1","author":"D Ge","year":"2019","unstructured":"Ge, D., Pan, Y., Shen, Z.J., Yuan, R., Zhang, C.: Retail supply chain management: a review of theories and practices. J. Data Inf. Manag. 1, 45\u201364 (2019). https:\/\/doi.org\/10.1007\/s42488-019-00004-z","journal-title":"J. Data Inf. Manag."},{"issue":"1","key":"16_CR6","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","volume":"37","author":"H Hewamalage","year":"2021","unstructured":"Hewamalage, H., Bergmeir, C., Bandara, K.: Recurrent neural networks for time series forecasting: current status and future directions. Int. J. Forecast. 37(1), 388\u2013427 (2021). https:\/\/doi.org\/10.1016\/j.ijforecast.2020.06.008","journal-title":"Int. J. Forecast."},{"key":"16_CR7","volume-title":"Forecasting: Principles and Practice","author":"R Hyndman","year":"2018","unstructured":"Hyndman, R., Athanasopoulos, G.: Forecasting: Principles and Practice, 2nd edn. OTexts, Australia (2018)","edition":"2"},{"key":"16_CR8","unstructured":"Jungbluth, A., Lederer, J.: The deepCAR method: forecasting time-series data that have change points (2023). https:\/\/arxiv.org\/abs\/2302.11241"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Karanikola, A., Liapis, C.M., Kotsiantis, S.: A comparison of contemporary methods on univariate time series forecasting. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C. (eds.) Advances in Machine Learning\/Deep Learning-based Technologies. Learning and Analytics in Intelligent Systems, vol. 23, pp. 143\u2013168. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-76794-5_8","DOI":"10.1007\/978-3-030-76794-5_8"},{"issue":"1","key":"16_CR10","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ijforecast.2019.05.011","volume":"36","author":"S Makridakis","year":"2020","unstructured":"Makridakis, S., Hyndman, R.J., Petropoulos, F.: Forecasting in social settings: the state of the art. Int. J. Forecast. 36(1), 15\u201328 (2020). https:\/\/doi.org\/10.1016\/j.ijforecast.2019.05.011","journal-title":"Int. J. Forecast."},{"key":"16_CR11","doi-asserted-by":"publisher","unstructured":"Papastefanopoulos, V., Linardatos, P., Kotsiantis, S.: COVID-19: a comparison of time series methods to forecast percentage of active cases per population. Appl. Sci. 10(11) (2020). https:\/\/doi.org\/10.3390\/app10113880","DOI":"10.3390\/app10113880"},{"issue":"3","key":"16_CR12","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). https:\/\/doi.org\/10.1016\/j.ijforecast.2019.07.001","journal-title":"Int. J. Forecast."},{"issue":"1","key":"16_CR13","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ijforecast.2018.12.007","volume":"36","author":"E Spiliotis","year":"2020","unstructured":"Spiliotis, E., Kouloumos, A., Assimakopoulos, V., Makridakis, S.: Are forecasting competitions data representative of the reality? Int. J. Forecast. 36(1), 37\u201353 (2020). https:\/\/doi.org\/10.1016\/j.ijforecast.2018.12.007","journal-title":"Int. J. Forecast."},{"issue":"3","key":"16_CR14","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1080\/01605682.2022.2118629","volume":"74","author":"S Makridakis","year":"2023","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V., Semenoglou, A.A., Mulder, G., Nikolopoulos, K.: Statistical, machine learning and deep learning forecasting methods: comparisons and ways forward. J. Oper. Res. Soc. 74(3), 840\u2013859 (2023). https:\/\/doi.org\/10.1080\/01605682.2022.2118629","journal-title":"J. Oper. Res. Soc."},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1016\/j.enconman.2019.06.024","volume":"196","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., et al.: Wind speed forecasting based on quantile regression minimal gated memory network and kernel density estimation. Energy Convers. Manage. 196, 1395\u20131409 (2019). https:\/\/doi.org\/10.1016\/j.enconman.2019.06.024","journal-title":"Energy Convers. Manage."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73503-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:15:36Z","timestamp":1731647736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73503-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"ISBN":["9783031735028","9783031735035"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73503-5_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,16]]},"assertion":[{"value":"16 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Viana do Castelo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"3 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2024.pt","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}