{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T04:03:04Z","timestamp":1773460984524,"version":"3.50.1"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,13]]},"DOI":"10.1109\/dsaa54385.2022.10032351","type":"proceedings-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T13:54:03Z","timestamp":1675864443000},"page":"1-10","source":"Crossref","is-referenced-by-count":8,"title":["Fractional SDE-Net: Generation of Time Series Data with Long-term Memory"],"prefix":"10.1109","author":[{"given":"Kohei","family":"Hayashi","sequence":"first","affiliation":[{"name":"The University of Tokyo,Graduate School of Mathematical Science,Tokyo,Japan"}]},{"given":"Kei","family":"Nakagawa","sequence":"additional","affiliation":[{"name":"Nomura Asset Management Co, Ltd.,Innovation Lab,Tokyo,Japan"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Latent odes for irregularly-sampled time series","author":"rubanova","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s007800050049"},{"key":"ref12","first-page":"770","article-title":"Deep residual learning for image recognition","author":"he","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10959-007-0083-0"},{"key":"ref15","volume":"113","author":"karatzas","year":"2012","journal-title":"Brownian Motion and Stochastic Calculus"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s007800300101"},{"key":"ref14","author":"pontryagin","year":"1987","journal-title":"Mathematical Theory of Optimal Processes"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9965.00057"},{"key":"ref31","first-page":"6696","article-title":"Neural controlled differential equations for irregular time series","volume":"33","author":"kidger","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref30","article-title":"Neural jump ordinary differential equations: Consistent continuous-time prediction and filtering","author":"herrera","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"ref11","first-page":"6572","article-title":"Neural ordinary differential equations","author":"chen","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/BF02401743"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA49011.2020.00051"},{"key":"ref32","first-page":"259","article-title":"Statistical methodology for nonperiodic cycles: from the covariance to r\/s analysis","volume":"1","author":"mandelbrot","year":"1972","journal-title":"Annals of Economic and Social Measurement"},{"key":"ref2","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3261-0"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1137\/1010093"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1142\/S021902490400258X"},{"key":"ref16","volume":"293","author":"revuz","year":"2013","journal-title":"Continuous Martingales and Brownian Motion"},{"key":"ref38","first-page":"1924","article-title":"The emergence of spectral universality in deep networks","author":"pennington","year":"2018","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1002\/9781119476771"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-797-8"},{"key":"ref24","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"2014","journal-title":"International Conference on Machine Learning"},{"key":"ref23","article-title":"Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit","author":"tzen","year":"2019"},{"key":"ref26","article-title":"Neural sdes as infinite-dimensional gans","author":"kidger","year":"2021"},{"key":"ref25","first-page":"5405","article-title":"Sde-net: Equipping deep neural networks with uncertainty estimates","author":"kong","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2012.09.003"},{"key":"ref22","first-page":"3084","article-title":"Theoretical guarantees for sampling and inference in generative models with latent diffusions","author":"tzen","year":"2019","journal-title":"Conference on Learning Theory"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(77)90006-X"},{"key":"ref28","first-page":"1126","article-title":"Infinitely deep neural networks as diffusion processes","author":"peluchetti","year":"2020","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref27","article-title":"Neural sde: Stabilizing neural ode networks with stochastic noise","author":"liu","year":"2019"},{"key":"ref29","first-page":"9847","article-title":"Neural jump stochastic differential equations","volume":"32","author":"jia","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3623086"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2020.1730426"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3490354.3494393"},{"key":"ref4","article-title":"C-rnn-gan: Continuous recurrent neural networks with adversarial training","author":"mogren","year":"2016"},{"key":"ref3","article-title":"A review on generative adversarial networks: Algorithms, theory, and applications","author":"gui","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"ref6","first-page":"5508","article-title":"Time-series generative adversarial networks","volume":"32","author":"yoon","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref5","article-title":"Real-valued (medical) time series generation with recurrent conditional gans","author":"esteban","year":"2017"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3470756"}],"event":{"name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","location":"Shenzhen, China","start":{"date-parts":[[2022,10,13]]},"end":{"date-parts":[[2022,10,16]]}},"container-title":["2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10032305\/10032324\/10032351.pdf?arnumber=10032351","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T13:58:02Z","timestamp":1682344682000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10032351\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/dsaa54385.2022.10032351","relation":{},"subject":[],"published":{"date-parts":[[2022,10,13]]}}}