{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T04:59:53Z","timestamp":1726030793933},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030205201"},{"type":"electronic","value":"9783030205218"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20521-8_12","type":"book-chapter","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T23:02:40Z","timestamp":1559689360000},"page":"139-151","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DeepTrace: A Generic Framework for Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Nithish B.","family":"Moudhgalya","sequence":"first","affiliation":[]},{"given":"Siddharth","family":"Divi","sequence":"additional","affiliation":[]},{"given":"V.","family":"Adithya Ganesan","sequence":"additional","affiliation":[]},{"given":"S.","family":"Sharan Sundar","sequence":"additional","affiliation":[]},{"given":"Vineeth","family":"Vijayaraghavan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"12_CR1","volume-title":"An Introduction to Bilinear Time Series Models","author":"CWJ Granger","year":"1978","unstructured":"Granger, C.W.J., Andersen, A.P.: An Introduction to Bilinear Time Series Models. Vandenhoeck & Ruprecht, G\u00f6ttingen (1978)"},{"key":"12_CR2","first-page":"987","volume":"50","author":"RF Engle","year":"1982","unstructured":"Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econom. J. Econom. Soc. 50, 987\u20131007 (1982)","journal-title":"Econom. J. Econom. Soc."},{"key":"12_CR3","volume-title":"Threshold Models in Non-linear Time Series Analysis","author":"H Tong","year":"2012","unstructured":"Tong, H.: Threshold Models in Non-linear Time Series Analysis, vol. 21. Springer, New York (2012)"},{"issue":"2","key":"12_CR4","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/0169-2070(92)90115-P","volume":"8","author":"JG Gooijer De","year":"1992","unstructured":"De Gooijer, J.G., Kumar, K.: Some recent developments in non-linear time series modelling, testing, and forecasting. Int. J. Forecast. 8(2), 135\u2013156 (1992)","journal-title":"Int. J. Forecast."},{"key":"12_CR5","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"7","key":"12_CR6","doi-asserted-by":"publisher","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS One 12(7), e0180944 (2017)","journal-title":"PloS One"},{"key":"12_CR7","unstructured":"Bi\u0144kowski, M., Marti, G., Donnat, P.: Autoregressive convolutional neural networks for asynchronous time series (2017). arXiv preprint: arXiv:1703.04122"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Borovykh, A., Bohte, S., Oosterlee, C.W.: Dilated convolutional neural networks for time series forecasting, March 2017","DOI":"10.21314\/JCF.2019.358"},{"key":"12_CR9","unstructured":"Cui, Z., Chen, W., Chen, Y.: Multi-scale convolutional neural networks for time series classification (2016). arXiv preprint: arXiv:1603.06995"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. CoRR, abs\/1411.4038 (2014)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"11","key":"12_CR11","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"Trans. Signal Process."},{"key":"12_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-319-08010-9_33","volume-title":"Web-Age Information Management","author":"Y Zheng","year":"2014","unstructured":"Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 298\u2013310. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08010-9_33"},{"key":"12_CR13","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: IJCAI, vol. 15, pp. 3995\u20134001 (2015)"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Sainath, T., Vinyals, O., Senior, A., Sak, H.: Convolutional, long short-term memory, fully connected deep neural networks. In: ICASSP (2015)","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T., Guo, T., Aberer, K.: Hybrid neural networks for learning the trend in time series. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 2273\u20132279 (2017)","DOI":"10.24963\/ijcai.2017\/316"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645\u20136649. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"issue":"8","key":"12_CR17","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"12_CR18","unstructured":"Pascanu, R., Gulcehre, C., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. In: Proceedings of the Second International Conference on Learning Representations (ICLR 2014) (2014)"},{"key":"12_CR19","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"G Peter Zhang","year":"2003","unstructured":"Peter Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159\u2013175 (2003)","journal-title":"Neurocomputing"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.-C., Yang, Y., Liu, H.: Modeling long- and short-term temporal patterns with deep neural networks. CoRR, abs\/1703.07015 (2017)","DOI":"10.1145\/3209978.3210006"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20521-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T03:05:10Z","timestamp":1608174310000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20521-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030205201","9783030205218"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20521-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"210","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"150","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"71% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}