{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:15:48Z","timestamp":1747152948109,"version":"3.40.5"},"publisher-location":"Cham","reference-count":2,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030683788"},{"type":"electronic","value":"9783030683795"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-68379-5_4","type":"book-chapter","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T14:06:33Z","timestamp":1614002793000},"page":"61-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Results"],"prefix":"10.1007","author":[{"given":"Tom\u00e9 Almeida","family":"Borges","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5482-9883","authenticated-orcid":false,"given":"Rui","family":"Neves","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"4_CR1","unstructured":"Magdon-Ismail M, Atiya AF (2015) An analysis of the maximum drawdown risk measure. Citeseer"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.asoc.2018.11.038","volume":"75","author":"DC Mallqui","year":"2019","unstructured":"Mallqui DC, Fernandes RA (2019) Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques. Appl Soft Comput 75:596\u2013606","journal-title":"Appl Soft Comput"}],"container-title":["SpringerBriefs in Applied Sciences and Technology","Financial Data Resampling for Machine Learning Based Trading"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68379-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T14:32:47Z","timestamp":1614004367000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-68379-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030683788","9783030683795"],"references-count":2,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68379-5_4","relation":{},"ISSN":["2191-530X","2191-5318"],"issn-type":[{"type":"print","value":"2191-530X"},{"type":"electronic","value":"2191-5318"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"23 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}