{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T23:33:18Z","timestamp":1785799998971,"version":"3.56.0"},"reference-count":133,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Euler Capital Pty Ltd, under APR Intern Agreement","award":["INT-0804"],"award-info":[{"award-number":["INT-0804"]}]},{"name":"APR.Intern and Australian National University"},{"name":"Australian Government Research Training Program (AGRTP) Domestic Scholarship"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>Volatility forecasting is an important aspect of finance as it dictates many decisions of market players. A snapshot of state-of-the-art neural network\u2013based financial volatility forecasting was generated by examining 35 studies, published after 2015. Several issues were identified, such as the inability for easy and meaningful comparisons, and the large gap between modern machine learning models and those applied to volatility forecasting. A shared task was proposed to evaluate state-of-the-art models, and several promising ways to bridge the gap were suggested. Finally, adequate background was provided to serve as an introduction to the field of neural network volatility forecasting.<\/jats:p>","DOI":"10.1145\/3483596","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T15:47:38Z","timestamp":1642434458000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Neural Network\u2013Based Financial Volatility Forecasting: A Systematic Review"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0429-3061","authenticated-orcid":false,"given":"Wenbo","family":"Ge","sequence":"first","affiliation":[{"name":"The Australian National University, Canberra, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pooia","family":"Lalbakhsh","sequence":"additional","affiliation":[{"name":"Monash University, Clayton VIC, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leigh","family":"Isai","sequence":"additional","affiliation":[{"name":"Euler Capital, Drysdale, Victoria, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artem","family":"Lenskiy","sequence":"additional","affiliation":[{"name":"The Australian National University, Canberra, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Suominen","sequence":"additional","affiliation":[{"name":"The Australian National University, Australia and Data61\/CSIRO, Canberra, ACT, Australia and University of Turku, Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Chicago Board Options Exchange. 2019. Cboe Volatility Index [White Paper]."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1990.137951"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011354913068"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.2307\/2527343"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(01)00055-1"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214501750332965"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.02.008"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12182-015-0035-8"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.5555\/1162264"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.5555\/525960"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJIDS.2015.068757"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(86)90063-1"},{"issue":"2","key":"e_1_3_2_14_2","first-page":"11","article-title":"Using Bollinger bands","volume":"10","author":"Bollinger John","year":"1992","unstructured":"John Bollinger. 1992. Using Bollinger bands. Stocks & Commodities 10, 2 (Feb. 1992), 11.","journal-title":"Stocks & Commodities"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.5555\/561899"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v44.n5.80"},{"issue":"3","key":"e_1_3_2_17_2","first-page":"321","article-title":"Multivariable functional interpolation and adaptive networks","volume":"2","author":"Broomhead D.","year":"1988","unstructured":"D. Broomhead and D. Lowe. 1988. Multivariable functional interpolation and adaptive networks. Complex Systems 2, 3 (1988), 321\u2013355.","journal-title":"Complex Systems"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1002\/for.2664"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2010.03.014"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0023378"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.02.006"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.1986.tb00501.x"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2011-000465"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1353\/mcb.2005.0027"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77117-5_83"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7750-1_74"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/59.544636"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(98)00034-8"},{"key":"e_1_3_2_29_2","volume-title":"NIPS 2014 Workshop on Deep Learning, December 2014","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014. arxiv:1412.3555."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1162\/003355300554692"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/fut.20148"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbp001"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.04.015"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.04.014"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2015.02.001"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.3905\/jod.1997.407971"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1209\/0295-5075\/4\/9\/004"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jimonfin.2003.09.011"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.2307\/1912773"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-075066942-9.50004-2"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.4903797"},{"key":"e_1_3_2_42_2","series-title":"Lecture Notes in Computer Science","first-page":"107","volume-title":"Mining Data for Financial Applications","author":"Fan Xiangru","year":"2020","unstructured":"Xiangru Fan, Xiaoqian Wei, Di Wang, Wen Zhang, and Wu Qi. 2020. Multi-step prediction of financial asset return volatility using parsimonious autoregressive sequential model. In Mining Data for Financial Applications, Lecture Notes in Computer Science, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Stefano Pascolutti, and Giovanni Ponti (Eds.). Springer International Publishing, Cham, 107\u2013121. https:\/\/doi.org\/10.1007\/978-3-030-37720-5_9"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0038-1667079"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0927-5398(98)00002-4"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015204"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00344251"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1086\/296072"},{"key":"e_1_3_2_48_2","first-page":"e01929","article-title":"Which features of postural sway are effective in distinguishing Parkinson\u2019s disease from controls? A systematic review","author":"Ge Wenbo","year":"2020","unstructured":"Wenbo Ge, Christian J. Lueck, Deborah Apthorp, and Hanna Suominen. 2020. Which features of postural sway are effective in distinguishing Parkinson\u2019s disease from controls? A systematic review. Brain and Behavior (Nov. 2020), e01929. https:\/\/doi.org\/10.1002\/brb3.1929","journal-title":"Brain and Behavior"},{"issue":"22","key":"e_1_3_2_49_2","first-page":"7","article-title":"Application of ANFIS-based CARRX model to stock volatility forecasting","volume":"17","author":"Geng Liyan","year":"2016","unstructured":"Liyan Geng and Zhanfu Zhang. 2016. Application of ANFIS-based CARRX model to stock volatility forecasting. International Journal of Simulation Systems, Science & Technology 17, 22 (Jan. 2016), 7.","journal-title":"International Journal of Simulation Systems, Science & Technology"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s007800050018"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112842"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1198\/073500105000000063"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1002\/jae.800"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA5771"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1080\/10920277.2001.10595984"},{"issue":"6","key":"e_1_3_2_57_2","first-page":"391","article-title":"Volatility prediction model for option pricing: A soft computing approach","volume":"10","author":"Harish Vijayalaxmi","year":"2015","unstructured":"Vijayalaxmi Harish, Chandrashekara S. Adiga, H. G. Joshi, and S. V. Harish. 2015. Volatility prediction model for option pricing: A soft computing approach. International Journal of Soft Computing 10, 6 (2015), 391\u2013399. https:\/\/doi.org\/10.3923\/ijscomp.2015.391.399","journal-title":"International Journal of Soft Computing"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.5555\/70405.70408"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1991.tb04646.x"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"e_1_3_2_64_2","unstructured":"Jacques Longerstaey and Martin Spencer. 1996. RiskMetrics Technical Document 4th ed."},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.2307\/1403192"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0949-2"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICICT.2017.8320178"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.03.002"},{"key":"e_1_3_2_70_2","first-page":"237","volume-title":"A Field Guide to Dynamical Recurrent Networks","author":"Kolen John F.","year":"2001","unstructured":"John F. Kolen and Stefan C. Kremer. 2001. Gradient flow in recurrent nets: The difficulty of learning LongTerm dependencies. In A Field Guide to Dynamical Recurrent Networks. IEEE, 237\u2013243. https:\/\/doi.org\/10.1109\/9780470544037.ch14"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.04.058"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.08.045"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.05.024"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v47.n4.22"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1109\/IADCC.2015.7154853","volume-title":"2015 IEEE International Advance Computing Conference (IACC\u201915)","author":"P. Hemanth Kumar","year":"2015","unstructured":"Hemanth Kumar P. and S. Basavaraj Patil. 2015. Estimation forecasting of volatility using ARIMA, ARFIMA and neural network based techniques. In 2015 IEEE International Advance Computing Conference (IACC\u201915). IEEE, 992\u2013997. https:\/\/doi.org\/10.1109\/IADCC.2015.7154853"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.02.025"},{"key":"e_1_3_2_78_2","volume-title":"IEEE International Conference on Neural Networks","author":"Lapedes Alan S.","year":"1987","unstructured":"Alan S. Lapedes and Robert Farber. 1987. Nonlinear signal processing using neural networks: Prediction and system modelling. In IEEE International Conference on Neural Networks."},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_81_2","volume-title":"6th International Conference on Learning Representations (ICLR\u201918)","author":"Lee Jaehoon","year":"2018","unstructured":"Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein. 2018. Deep neural networks as gGaussian processes. In 6th International Conference on Learning Representations (ICLR\u201918). arxiv:1711.00165."},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454758"},{"key":"e_1_3_2_83_2","unstructured":"Bryan Lim Sercan O. Arik Nicolas Loeff and Tomas Pfister. 2020. Temporal fusion transformers for interpretable multi-horizon time series forecasting. arxiv:1912.09363 [cs stat]."},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.548162"},{"issue":"3","key":"e_1_3_2_85_2","first-page":"27","article-title":"Chaotic behavior in financial market volatility","volume":"21","author":"Litimi Houda","year":"2018","unstructured":"Houda Litimi, Ahmed BenSaida, Lotfi Belkacem, and Oussama Abdallah. 2018. Chaotic behavior in financial market volatility. Journal of Risk 21, 3 (2018), 27\u201353. https:\/\/doi.org\/10.21314\/JOR.2018.400","journal-title":"Journal of Risk"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.04.038"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504819"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-349-20213-3_13"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-017-0056-6"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.5555\/972470.972475"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.1804070"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v51.n4.1916"},{"key":"e_1_3_2_93_2","unstructured":"Bryan McCann Nitish Shirish Keskar Caiming Xiong and Richard Socher. 2018. The natural language decathlon: Multitask learning as question answering. arxiv:1806.08730 [cs stat]."},{"key":"e_1_3_2_94_2","unstructured":"Stephen Merity Caiming Xiong James Bradbury and Richard Socher. 2016. Pointer sentinel mixture models. arxiv:1609.07843 [cs]"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1000097"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.09.155"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.24818\/18423264\/53.2.19.05"},{"key":"e_1_3_2_98_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/978-3-319-25261-2_8","volume-title":"Artificial Intelligence in Theory and Practice IV","author":"Mostafa Fahed","year":"2015","unstructured":"Fahed Mostafa, Tharam Dillon, and Elizabeth Chang. 2015. Computational intelligence approach to capturing the implied volatility. In Artificial Intelligence in Theory and Practice IV, Tharam Dillon (Ed.). Vol. 465. Springer International Publishing, Cham, 85\u201397. https:\/\/doi.org\/10.1007\/978-3-319-25261-2_8"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-020-00177-2"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.09.048"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1086\/296071"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2010.03.034"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1002\/isaf.1455"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.1257\/jel.41.2.478"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.04.014"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pacfin.2018.06.002"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.03.046"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoap\/1177005835"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1080\/758526905"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1002\/0470870168.ch1"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v46.n3.23"},{"key":"e_1_3_2_114_2","volume-title":"Proceedings of ICML (Workshop on Theoretical Foundations and Applications of Deep Generative Models)","author":"Sengupta Biswa","year":"2018","unstructured":"Biswa Sengupta and Karl J. Friston. 2018. How robust are deep neural networks?. In Proceedings of ICML (Workshop on Theoretical Foundations and Applications of Deep Generative Models)."},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106181"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.157290"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295059"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1002\/fut.20197"},{"key":"e_1_3_2_119_2","first-page":"17","volume-title":"Workshop on MIning DAta for Financial Applications (MIDAS@PKDD\/ECML)","author":"Stefani Jacopo De","year":"2017","unstructured":"Jacopo De Stefani, Olivier Caelen, Dalila Hattab, and Gianluca Bontempi. 2017. Machine learning for multi-step ahead forecasting of volatility proxies. In Workshop on MIning DAta for Financial Applications (MIDAS@PKDD\/ECML). 17\u201328."},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0219024917500480"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.2196\/10961"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2017.06.012"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1002\/1099-131X(200007)19:4<299::AID-FOR775>3.0.CO;2-V"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1080\/02664760600994539"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2070(03)00012-8"},{"key":"e_1_3_2_126_2","unstructured":"Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner Andrew Senior and Koray Kavukcuoglu. 2016. WaveNet: A generative model for raw audio. arxiv:1609.03499 [cs]."},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113481"},{"key":"e_1_3_2_129_2","first-page":"8","volume-title":"Workshops at the 29th AAAI Conference on Artificial Intelligence","author":"Wang Zhiguang","year":"2015","unstructured":"Zhiguang Wang and Tim Oates. 2015. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Workshops at the 29th AAAI Conference on Artificial Intelligence. 8."},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(90)90004-5"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1086\/209650"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2019.1711148"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105739"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3483596","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3483596","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:19:03Z","timestamp":1750191543000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3483596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":133,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,31]]}},"alternative-id":["10.1145\/3483596"],"URL":"https:\/\/doi.org\/10.1145\/3483596","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,17]]},"assertion":[{"value":"2020-12-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}