{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:04:48Z","timestamp":1740099888977,"version":"3.37.3"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100007170","name":"Ministry of Economy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007170","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,1]]},"DOI":"10.1109\/ssci47803.2020.9308368","type":"proceedings-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T23:12:38Z","timestamp":1609888358000},"page":"2625-2632","source":"Crossref","is-referenced-by-count":4,"title":["Investigating RNNs for vehicle volume forecasting in service stations"],"prefix":"10.1109","author":[{"given":"Himadri Sikhar","family":"Khargharia","sequence":"first","affiliation":[]},{"given":"Roberto","family":"Santana","sequence":"additional","affiliation":[]},{"given":"Siddhartha","family":"Shakya","sequence":"additional","affiliation":[]},{"given":"Russell","family":"Ainslie","sequence":"additional","affiliation":[]},{"given":"Gilbert","family":"Owusu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref11","article-title":"Time series forecasting based on augmented long short-term memory","volume":"abs 1707 666","author":"hsu","year":"2017","journal-title":"CoRR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/COGSIMA.2019.8724239"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34885-4_14"},{"key":"ref14","article-title":"Adam: A method for stochastic optimization","volume":"abs 1412 6980","author":"kingma","year":"2014","journal-title":"CoRR"},{"key":"ref15","article-title":"Patterns of dataset shift","author":"kull","year":"2014","journal-title":"First International Workshop on Learning over Multiple Contexts (LMCE) at ECML-PKDD"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1023\/B:WARM.0000024727.94701.12"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref18","first-page":"1","article-title":"A symmetric grammar approach for designing segmentation models","author":"lima","year":"2020","journal-title":"2020 IEEE Congress on Evolutionary Computation (CEC)"},{"key":"ref19","article-title":"Recurrent Neural Networks for Prediction: Learning Algorithms","author":"mandic","year":"2001","journal-title":"Architectures and Stability"},{"key":"ref28","article-title":"ADADELTA: an adaptive learning rate method","volume":"abs 1212 5701","author":"zeiler","year":"2012","journal-title":"CoRR"},{"journal-title":"F Chollet Keras","year":"2015","key":"ref4"},{"key":"ref27","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"tieleman","year":"2012","journal-title":"COURSERA Neural Networks for Machine Learning"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref6","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref29","first-page":"1","article-title":"Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network","author":"zheng","year":"2017","journal-title":"2017 51st Annual Conference on Information Sciences and Systems (CISS) CISS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2011.04.001"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3205455.3205550"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.07.026"},{"key":"ref2","article-title":"An overview and comparative analysis of recurrent neural networks for short term load forecasting","volume":"abs 1705 4378","author":"bianchi","year":"2017","journal-title":"CoRR"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0219-9_20"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3225-z"},{"key":"ref20","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","author":"pascanu","year":"2013","journal-title":"International Conference on Machine Learning"},{"key":"ref22","article-title":"From nodes to networks: Evolving recurrent neural networks","volume":"abs 1803 4439","author":"rawal","year":"2018","journal-title":"CoRR"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1080\/19368623.2020.1722304"},{"key":"ref24","article-title":"An overview of gradient descent optimization algorithms","volume":"abs 1609 4747","author":"ruder","year":"2016","journal-title":"CoRR"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref26","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref25","article-title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks","volume":"abs 1704 4110","author":"salinas","year":"2017","journal-title":"CoRR"}],"event":{"name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","start":{"date-parts":[[2020,12,1]]},"location":"Canberra, ACT, Australia","end":{"date-parts":[[2020,12,4]]}},"container-title":["2020 IEEE Symposium Series on Computational Intelligence (SSCI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9308061\/9308107\/09308368.pdf?arnumber=9308368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T15:17:25Z","timestamp":1656602245000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9308368\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/ssci47803.2020.9308368","relation":{},"subject":[],"published":{"date-parts":[[2020,12,1]]}}}