{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T10:30:05Z","timestamp":1759228205482,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031734991"},{"type":"electronic","value":"9783031735004"}],"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\/978-3-031-73500-4_28","type":"book-chapter","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T03:59:59Z","timestamp":1731643199000},"page":"335-346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Time Series Data Augmentation as\u00a0an\u00a0Imbalanced Learning Problem"],"prefix":"10.1007","author":[{"given":"Vitor","family":"Cerqueira","sequence":"first","affiliation":[]},{"given":"Nuno","family":"Moniz","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"In\u00e1cio","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Soares","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"issue":"4","key":"28_CR1","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TNNLS.2020.2985720","volume":"32","author":"K Bandara","year":"2020","unstructured":"Bandara, K., Bergmeir, C., Hewamalage, H.: Lstm-msnet: leveraging forecasts on sets of related time series with multiple seasonal patterns. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 1586\u20131599 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"28_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108148","volume":"120","author":"K Bandara","year":"2021","unstructured":"Bandara, K., Hewamalage, H., Liu, Y.H., Kang, Y., Bergmeir, C.: Improving the accuracy of global forecasting models using time series data augmentation. Pattern Recogn. 120, 108148 (2021)","journal-title":"Pattern Recogn."},{"issue":"1","key":"28_CR3","first-page":"2653","volume":"18","author":"A Benavoli","year":"2017","unstructured":"Benavoli, A., Corani, G., Dem\u0161ar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis. J. Mach. Learn. Res. 18(1), 2653\u20132688 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Bontempi, G., Ben\u00a0Taieb, S., Le\u00a0Borgne, Y.A.: Machine learning strategies for time series forecasting. In: Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, 15\u201321 July 2012, Tutorial Lectures 2, pp. 62\u201377 (2013)","DOI":"10.1007\/978-3-642-36318-4_3"},{"issue":"2","key":"28_CR5","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10844-022-00713-9","volume":"59","author":"V Cerqueira","year":"2022","unstructured":"Cerqueira, V., Torgo, L., Soares, C.: A case study comparing machine learning with statistical methods for time series forecasting: size matters. J. Intell. Inf. Syst. 59(2), 415\u2013433 (2022)","journal-title":"J. Intell. Inf. Syst."},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"28_CR7","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez, A., Garcia, S., Herrera, F., Chawla, N.V.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863\u2013905 (2018)","journal-title":"J. Artif. Intell. Res."},{"issue":"1","key":"28_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/for.3980040103","volume":"4","author":"ES Gardner Jr","year":"1985","unstructured":"Gardner, E.S., Jr.: Exponential smoothing: the state of the art. J. Forecast. 4(1), 1\u201328 (1985)","journal-title":"J. Forecast."},{"key":"28_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107518","volume":"233","author":"R Godahewa","year":"2021","unstructured":"Godahewa, R., Bandara, K., Webb, G.I., Smyl, S., Bergmeir, C.: Ensembles of localised models for time series forecasting. Knowl.-Based Syst. 233, 107518 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11538059_91"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328. IEEE (2008)","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"28_CR12","unstructured":"Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts (2018)"},{"issue":"1","key":"28_CR13","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.ijforecast.2019.05.008","volume":"36","author":"T Januschowski","year":"2020","unstructured":"Januschowski, T., et al.: Criteria for classifying forecasting methods. Int. J. Forecast. 36(1), 167\u2013177 (2020)","journal-title":"Int. J. Forecast."},{"issue":"1","key":"28_CR14","first-page":"21","volume":"22","author":"KB Kahn","year":"2003","unstructured":"Kahn, K.B.: How to measure the impact of a forecast error on an enterprise? J. Bus. Forecast. 22(1), 21 (2003)","journal-title":"J. Bus. Forecast."},{"issue":"4","key":"28_CR15","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1016\/j.ijforecast.2021.11.013","volume":"38","author":"S Makridakis","year":"2022","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: M5 accuracy competition: results, findings, and conclusions. Int. J. Forecast. 38(4), 1346\u20131364 (2022)","journal-title":"Int. J. Forecast."},{"key":"28_CR16","unstructured":"Mani, I., Zhang, I.: knn approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of Workshop on Learning from Imbalanced Datasets, vol.\u00a0126. ICML United States (2003)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Sousa, J., Henriques, R.: Intersecting reinforcement learning and deep factor methods for optimizing locality and globality in forecasting: a review (2023)","DOI":"10.21203\/rs.3.rs-2570163\/v1"}],"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\/978-3-031-73500-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T17:25:46Z","timestamp":1743182746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73500-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,16]]},"ISBN":["9783031734991","9783031735004"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73500-4_28","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"}}]}}