{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:41:07Z","timestamp":1725860467006},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319419190"},{"type":"electronic","value":"9783319419206"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-41920-6_22","type":"book-chapter","created":{"date-parts":[[2016,6,27]],"date-time":"2016-06-27T01:11:43Z","timestamp":1466989903000},"page":"294-310","source":"Crossref","is-referenced-by-count":4,"title":["DSCo: A Language Modeling Approach for Time Series Classification"],"prefix":"10.1007","author":[{"given":"Daoyuan","family":"Li","sequence":"first","affiliation":[]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tegawend\u00e9 F.","family":"Bissyand\u00e9","sequence":"additional","affiliation":[]},{"given":"Jacques","family":"Klein","sequence":"additional","affiliation":[]},{"given":"Yves","family":"Le Traon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,6,28]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, K., Ocneanu, A., Benitez, D., Carlson, D., Rowe, A., Berges, M.: Blued: a fully labeled public dataset for event-based non-intrusive load monitoring research. In: Proceedings of the 2nd KDD Workshop on Data Mining Applications in Sustainability, pp. 1\u20135 (2012)","DOI":"10.1109\/IECON.2012.6389367"},{"key":"22_CR2","first-page":"699","volume":"11","author":"GE Batista","year":"2011","unstructured":"Batista, G.E., Wang, X., Keogh, E.J.: A complexity-invariant distance measure for time series. SDM 11, 699\u2013710 (2011)","journal-title":"SDM"},{"issue":"11","key":"22_CR3","doi-asserted-by":"crossref","first-page":"2796","DOI":"10.1109\/TPAMI.2013.72","volume":"35","author":"MG Baydogan","year":"2013","unstructured":"Baydogan, M.G., Runger, G., Tuv, E.: A bag-of-features framework to classify time series. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(11), 2796\u20132802 (2013)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"22_CR4","unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, pp. 359\u2013370 (1994)"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., \u00d6zsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 491\u2013502. ACM (2005)","DOI":"10.1145\/1066157.1066213"},{"key":"22_CR6","unstructured":"Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive, July 2015. www.cs.ucr.edu\/~eamonn\/time_series_data\/"},{"issue":"1","key":"22_CR7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"TC Fu","year":"2011","unstructured":"Fu, T.C.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24(1), 164\u2013181 (2011)","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"4","key":"22_CR8","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10618-013-0322-1","volume":"28","author":"J Hills","year":"2014","unstructured":"Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery 28(4), 851\u2013881 (2014)","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"3","key":"22_CR9","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/PL00011669","volume":"3","author":"E Keogh","year":"2001","unstructured":"Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263\u2013286 (2001)","journal-title":"Knowledge and Information Systems"},{"key":"22_CR10","unstructured":"Keogh, E., Wei, L., Xi, X., Lee, S.H., Vlachos, M.: Lb_keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 882\u2013893. VLDB Endowment (2006)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285\u2013289. ACM (2000)","DOI":"10.1145\/347090.347153"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Li, D., Bissyand\u00e9, T.F., Kubler, S., Klein, J., Le Traon, Y.: Profiling household appliance electricity usage with n-gram language modeling. In: The 2016 IEEE International Conference on Industrial Technology (ICIT 2016). IEEE, Taipei, March 2016","DOI":"10.1109\/ICIT.2016.7474818"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Lin, J.: Approximate variable-length time series motif discovery using grammar inference. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, p. 10 (2010)","DOI":"10.1145\/1814245.1814255"},{"issue":"2","key":"22_CR14","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15(2), 107\u2013144 (2007)","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"2","key":"22_CR15","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TPAMI.2008.76","volume":"31","author":"PF Marteau","year":"2009","unstructured":"Marteau, P.F.: Time warp edit distance with stiffness adjustment for time series matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 306\u2013318 (2009)","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Mueen, A., Keogh, E., Young, N.: Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1154\u20131162. ACM (2011)","DOI":"10.1145\/2020408.2020587"},{"key":"22_CR17","unstructured":"Norvig, P.: Natural language corpus data. In: Segaran, T., Hammerbacher, J. (eds.) Beautiful Data: The Stories Behind Elegant Data Solutions, pp. 219\u2013242. O\u2019Reilly Media, Inc. (2009)"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275\u2013281 (1998)","DOI":"10.1145\/290941.291008"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the Thirteenth SIAM Conference on Data Mining (2013)","DOI":"10.1137\/1.9781611972832.74"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Ratanamahatana, C.A., Keogh, E.: Three myths about dynamic time warping data mining. In: Proceedings of SIAM International Conference on Data Mining, pp. 506\u2013510 (2005)","DOI":"10.1137\/1.9781611972757.50"},{"key":"22_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/978-3-662-44845-8_37","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"P Senin","year":"2014","unstructured":"Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A.P., Chen, C., Frankenstein, S., Lerner, M.: GrammarViz 2.0: a tool for grammar-based pattern discovery in time series. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014, Part III. LNCS, vol. 8726, pp. 468\u2013472. Springer, Heidelberg (2014)"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Senin, P., Malinchik, S.: Sax-vsm: interpretable time series classification using sax and vector space model. In: IEEE 13th International Conference on Data Mining, pp. 1175\u20131180. IEEE (2013)","DOI":"10.1109\/ICDM.2013.52"},{"key":"22_CR23","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.knosys.2014.04.035","volume":"67","author":"J Serr\u00e0","year":"2014","unstructured":"Serr\u00e0, J., Arcos, J.L.: An empirical evaluation of similarity measures for time series classification. Knowledge-Based Systems 67, 305\u2013314 (2014)","journal-title":"Knowledge-Based Systems"},{"key":"22_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1007\/978-3-642-32986-9_31","volume-title":"Case-Based Reasoning Research and Development","author":"J Serr\u00e0","year":"2012","unstructured":"Serr\u00e0, J., Arcos, J.L.: A competitive measure to assess the similarity between two time series. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 414\u2013427. Springer, Heidelberg (2012)"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Smith, A.A., Craven, M.: Fast multisegment alignments for temporal expression profiles. In: Proceedings of the 7th Annual International Conference on Computational Systems Bioinformatics, vol. 7, pp. 315\u2013326. World Scientific (2008)","DOI":"10.1142\/9781848162648_0028"},{"issue":"2","key":"22_CR26","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TIT.1967.1054010","volume":"13","author":"AJ Viterbi","year":"1967","unstructured":"Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260\u2013269 (1967)","journal-title":"IEEE Transactions on Information Theory"},{"issue":"2","key":"22_CR27","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10618-012-0250-5","volume":"26","author":"X Wang","year":"2013","unstructured":"Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery 26(2), 275\u2013309 (2013)","journal-title":"Data Mining and Knowledge Discovery"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Wijaya, T.K., Eberle, J., Aberer, K.: Symbolic representation of smart meter data. In: Proceedings of the Joint EDBT\/ICDT 2013 Workshops, pp. 242\u2013248. ACM (2013)","DOI":"10.1145\/2457317.2457357"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 1033\u20131040. ACM (2006)","DOI":"10.1145\/1143844.1143974"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947\u2013956. ACM (2009)","DOI":"10.1145\/1557019.1557122"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Data Mining in Pattern Recognition"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-41920-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T06:00:18Z","timestamp":1656741618000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-41920-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319419190","9783319419206"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-41920-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}