{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:14:33Z","timestamp":1763018073867,"version":"3.37.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304836"},{"type":"electronic","value":"9783030304843"}],"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\/978-3-030-30484-3_51","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T23:02:47Z","timestamp":1567983767000},"page":"639-651","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-5677","authenticated-orcid":false,"given":"Muhammad Ali","family":"Chattha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4600-7331","authenticated-orcid":false,"given":"Shoaib Ahmed","family":"Siddiqui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7818-0361","authenticated-orcid":false,"given":"Mohsin","family":"Munir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Imran","family":"Malik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ludger","family":"van Elst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-6520","authenticated-orcid":false,"given":"Sheraz","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"51_CR1","unstructured":"Bandara, K., Bergmeir, C., Smyl, S.: Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. arXiv preprint arXiv:1710.03222 (2017)"},{"issue":"2","key":"51_CR2","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ijforecast.2015.07.002","volume":"32","author":"C Bergmeir","year":"2016","unstructured":"Bergmeir, C., Hyndman, R.J., Ben\u00edtez, J.M.: Bagging exponential smoothing methods using STL decomposition and box-cox transformation. Int. J. Forecast. 32(2), 303\u2013312 (2016)","journal-title":"Int. J. Forecast."},{"key":"51_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/978-3-319-93034-3_46","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"TS Buda","year":"2018","unstructured":"Buda, T.S., Caglayan, B., Assem, H.: DeepAD: a generic framework based on deep learning for time series anomaly detection. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10937, pp. 577\u2013588. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93034-3_46"},{"key":"51_CR4","unstructured":"Chattha, M.A., Siddiqui, S.A., Malik, M.I., van Elst, L., Dengel, A., Ahmed, S.: Kinn. arXiv preprint arXiv:1902.05653 (2019)"},{"key":"51_CR5","unstructured":"Columbus, L.: Gartner\u2019s hype cycle for emerging technologies, 2017 adds 5g and deep learning for first time. Forbes\/Tech\/# CuttingEdge (2017)"},{"key":"51_CR6","doi-asserted-by":"crossref","unstructured":"Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364 (2017)","DOI":"10.18653\/v1\/D17-1070"},{"key":"51_CR7","doi-asserted-by":"crossref","unstructured":"Ghazvininejad, M., et al.: A knowledge-grounded neural conversation model. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11977"},{"issue":"6","key":"51_CR8","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82\u201397 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"51_CR9","doi-asserted-by":"crossref","unstructured":"Hu, Z., Ma, X., Liu, Z., Hovy, E., Xing, E.: Harnessing deep neural networks with logic rules. arXiv preprint arXiv:1603.06318 (2016)","DOI":"10.18653\/v1\/P16-1228"},{"issue":"6266","key":"51_CR10","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1126\/science.aab3050","volume":"350","author":"BM Lake","year":"2015","unstructured":"Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332\u20131338 (2015)","journal-title":"Science"},{"issue":"4","key":"51_CR11","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0169-2070(00)00057-1","volume":"16","author":"S Makridakis","year":"2000","unstructured":"Makridakis, S., Hibon, M.: The M3-Competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451\u2013476 (2000)","journal-title":"Int. J. Forecast."},{"issue":"4","key":"51_CR12","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.ijforecast.2018.06.001","volume":"34","author":"S Makridakis","year":"2018","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The M4 Competition: results, findings, conclusion and way forward. Int. J. Forecast. 34(4), 802\u2013808 (2018)","journal-title":"Int. J. Forecast."},{"issue":"11","key":"51_CR13","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.3390\/s19112451","volume":"19","author":"M Munir","year":"2019","unstructured":"Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019)","journal-title":"Sensors"},{"issue":"5","key":"51_CR14","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1002\/(SICI)1099-131X(199909)18:5<359::AID-FOR746>3.0.CO;2-P","volume":"18","author":"M Nelson","year":"1999","unstructured":"Nelson, M., Hill, T., Remus, W., O\u2019Connor, M.: Time series forecasting using neural networks: should the data be deseasonalized first? Journal of forecasting 18(5), 359\u2013367 (1999)","journal-title":"Journal of forecasting"},{"issue":"7676","key":"51_CR15","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354 (2017)","journal-title":"Nature"},{"issue":"8","key":"51_CR16","doi-asserted-by":"publisher","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","volume":"39","author":"SB Taieb","year":"2012","unstructured":"Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067\u20137083 (2012)","journal-title":"Expert Syst. Appl."},{"issue":"1\u20132","key":"51_CR17","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/0004-3702(94)90105-8","volume":"70","author":"GG Towell","year":"1994","unstructured":"Towell, G.G., Shavlik, J.W.: Knowledge-based artificial neural networks. Artif. Intell. 70(1\u20132), 119\u2013165 (1994)","journal-title":"Artif. Intell."},{"issue":"2","key":"51_CR18","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1109\/TNNLS.2016.2603784","volume":"29","author":"SN Tran","year":"2018","unstructured":"Tran, S.N., Garcez, A.S.D.: Deep logic networks: inserting and extracting knowledge from deep belief networks. IEEE Trans. Neural Networks Learn. Syst. 29(2), 246\u2013258 (2018)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"51_CR19","volume-title":"Principles of Database and Knowledge-base Systems","author":"JD Ullman","year":"1988","unstructured":"Ullman, J.D.: Principles of Database and Knowledge-base Systems, vol. 1. Computer Science Press Incorporated, Rockville (1988)"},{"key":"51_CR20","doi-asserted-by":"crossref","unstructured":"Venugopalan, S., Hendricks, L.A., Mooney, R., Saenko, K.: Improving lstm-based video description with linguistic knowledge mined from text. arXiv preprint arXiv:1604.01729 (2016)","DOI":"10.18653\/v1\/D16-1204"},{"key":"51_CR21","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Deep Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30484-3_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:19:08Z","timestamp":1695169148000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30484-3_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304836","9783030304843"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30484-3_51","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":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}