{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T10:45:17Z","timestamp":1782729917704,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["18\/CRT\/6183"],"award-info":[{"award-number":["18\/CRT\/6183"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Insight SFI Research Centre for Data Analytics","doi-asserted-by":"publisher","award":["FI\/12\/RC\/2289 P2"],"award-info":[{"award-number":["FI\/12\/RC\/2289 P2"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,29]]},"DOI":"10.1145\/3641142.3641143","type":"proceedings-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T13:21:54Z","timestamp":1715606514000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing Time Series Data Predictions: A Survey of Augmentation Techniques and Model Performances"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4711-9055","authenticated-orcid":false,"given":"Alexander Okhuese","family":"Victor","sequence":"first","affiliation":[{"name":"ML Labs SFI Centre for Research Training in Machine Learning, School of Electronic Engineering, Dublin City University, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0674-2131","authenticated-orcid":false,"given":"Muhammad Intizar","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Dublin City University, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104930"},{"key":"e_1_3_2_1_2_1","volume-title":"Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs","year":"2021","unstructured":"C, Esteban, S, Hyland, and G, R\u00e4tsch, \u201cReal-valued (Medical) Time Series Generation with Recurrent Conditional GANs.\u201d, 2021 ResearchGate. https:\/\/www.researchgate.net\/publication\/317418923_"},{"key":"e_1_3_2_1_3_1","volume-title":"TimeNet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838","author":"Malhotra P.","year":"2017","unstructured":"P. Malhotra, V. TV, L. Vig, P. Agarwal, and G. Shroff. TimeNet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838, 2017."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3145951"},{"key":"e_1_3_2_1_5_1","volume-title":"the 36th international symposium on forecasting","author":"Smyl S.","year":"2016","unstructured":"S. Smyl, and K. Kuber. \u201cData preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks.\u201d In the 36th international symposium on forecasting, 2016."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","unstructured":"K. Huang Greeneyes: An Air Pollution Evaluation System based on WaveNet. doi:10.14711\/thesis-991012980217003412","DOI":"10.14711\/thesis-991012980217003412"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21244-4_11"},{"key":"e_1_3_2_1_8_1","volume-title":"Transformer-based conditional generative adversarial network for multivariate time series generation. arXiv.org. https:\/\/arxiv.org\/abs\/2210.02089","author":"Madane A.","year":"2022","unstructured":"A. Madane. Transformer-based conditional generative adversarial network for multivariate time series generation. arXiv.org. https:\/\/arxiv.org\/abs\/2210.02089, 2022."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3261-3"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.17018"},{"key":"e_1_3_2_1_11_1","volume-title":"2016 13th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-4).","author":"Pan Y.","year":"2016","unstructured":"Y. Pan, M. Zhang, Z. Chen, M. Zhou, and Z. Zhang. \u201cAn ARIMA based model for forecasting the patient number of epidemic disease.\u201d In 2016 13th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1-4). 2016, IEEE."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-015-0962-6"},{"key":"e_1_3_2_1_13_1","volume-title":"\u201cA new variational autoencoder based model for time series anomaly detection","author":"Zhang C.","year":"1907","unstructured":"C. Zhang, S. Li, H, Zhang, and Y Chen, Y. Velc: \u201cA new variational autoencoder based model for time series anomaly detection.\u201d arXiv preprint arXiv:1907.01702, 2019."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3041833","article-title":"EVDHM-ARIMA-based time series forecasting model and its application for COVID-19 cases","volume":"70","author":"Sharma R. R.","year":"2020","unstructured":"R. R. Sharma, M. Kumar, S. Maheshwari and K. P. Ray. EVDHM-ARIMA-based time series forecasting model and its application for COVID-19 cases. IEEE Transactions on Instrumentation and Measurement, 2020, 70, 1-10.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.56042\/jsir.v81i04.50791"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115532"},{"key":"e_1_3_2_1_17_1","volume-title":"The 2nd International Conference on Software Engineering and Data Mining (pp. 589-596)","author":"Wu W.","year":"2010","unstructured":"W. Wu, W. Zhang, Y. Yang, and Q. Wang. Time series analysis for bug number prediction. In The 2nd International Conference on Software Engineering and Data Mining (pp. 589-596).2010, IEEE."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-0878-0_35"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02602-x"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.2019.1800155"},{"key":"e_1_3_2_1_21_1","first-page":"2015","volume-title":"ESANN (Vol.","author":"Malhotra P.","year":"2015","unstructured":"P. Malhotra, L. Vig, G. Shroff and P. Agarwal. \u201cLong Short-Term Memory Networks for Anomaly Detection in Time Series.\u201d In ESANN (Vol. 2015, p. 89), 2015."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1039\/D0EE02970J"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-019-00330-1"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106682"},{"key":"e_1_3_2_1_25_1","unstructured":"T. Che Y. Li AP. Jacob Y. Bengio W. Li. Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136. 2016 Dec 7."},{"key":"e_1_3_2_1_26_1","unstructured":"A. Srivastava L. Valkov C. Russell MU. Gutmann C. Sutton Veegan: Reducing mode collapse in gans using implicit variational learning. Advances in neural information processing systems. 2017;30."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3178592"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-22953-4_3"},{"key":"e_1_3_2_1_29_1","volume-title":"Velc: A new variational autoencoder based model for time series anomaly detection. arXiv preprint arXiv:2019","author":"Zhang C.","year":"1907","unstructured":"C. Zhang, S. Li, H. Zhang, and Y. Chen. Velc: A new variational autoencoder based model for time series anomaly detection. arXiv preprint arXiv:2019. 1907.01702."},{"key":"e_1_3_2_1_30_1","volume-title":"2021 7th International Conference on Optimization and Applications (ICOA) (pp. 1-6). IEEE.","author":"Goubeaud M.","unstructured":"M. Goubeaud, P. Jou\u00dfen, N. Gmyrek, F. Ghorban, L. Schelkes, and A. Kummert. Using Variational Autoencoder to augment Sparse Time series Datasets. In 2021 7th International Conference on Optimization and Applications (ICOA) (pp. 1-6). IEEE."},{"key":"e_1_3_2_1_31_1","volume-title":"the 36th international symposium on forecasting. 2016","author":"Smyl S.","unstructured":"S. Smyl, and K. Kuber. Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks. In the 36th international symposium on forecasting. 2016"},{"key":"e_1_3_2_1_32_1","first-page":"441","article-title":"A review of irregular time series data handling with gated recurrent neural networks","author":"Weerakkody P. B.","year":"2021","unstructured":"P. B. Weerakkody, K. W. Wong, G. Wang, and W. Ela. A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing, 441, 2021. 161-178.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412812"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3145951"},{"key":"e_1_3_2_1_35_1","volume-title":"TimeNet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838","author":"Malhotra P.","year":"2017","unstructured":"P. Malhotra, V. TV, L. Vig, P. Agarwal, and G. Shroff (2017). TimeNet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838."},{"key":"e_1_3_2_1_36_1","volume-title":"Time-series generative adversarial networks. Advances in neural information processing systems","author":"Yoon J.","year":"2019","unstructured":"J. Yoon, D. Jarrett, and M. Van der Schaar. Time-series generative adversarial networks. Advances in neural information processing systems, 2019. 32."},{"key":"e_1_3_2_1_37_1","volume-title":"Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25\u201328, 2020, Proceedings 18 (pp. 382-391)","author":"Dash S.","unstructured":"S. Dash, A. Yale, I. Guyon, and K. P. Bennett. Medical time-series data generation using generative adversarial networks. In Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25\u201328, 2020, Proceedings 18 (pp. 382-391). Springer International Publishing."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physletb.2020.135628"}],"event":{"name":"ACSW 2024: 2024 Australasian Computer Science Week","location":"Sydney NSW Australia","acronym":"ACSW 2024"},"container-title":["Proceedings of the 2024 Australasian Computer Science Week"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641142.3641143","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3641142.3641143","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T02:15:42Z","timestamp":1755915342000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3641142.3641143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":38,"alternative-id":["10.1145\/3641142.3641143","10.1145\/3641142"],"URL":"https:\/\/doi.org\/10.1145\/3641142.3641143","relation":{},"subject":[],"published":{"date-parts":[[2024,1,29]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}