{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T23:02:47Z","timestamp":1773615767442,"version":"3.50.1"},"reference-count":29,"publisher":"Allerton Press","issue":"2","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Aut. Control Comp. Sci."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.3103\/s0146411622020067","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T14:03:13Z","timestamp":1652882593000},"page":"120-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time Series Similarity Search Methods for Sensor Data"],"prefix":"10.3103","volume":"56","author":[{"family":"Anupama Jawale","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Ganesh Magar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1627","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"key":"7471_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v062.i01","volume":"62","author":"P. Montero","year":"2014","unstructured":"Montero, P. and Vilar, J.A., TSclust: An R Package for time series clustering, J. Stat. Software, 2014, vol. 62, no.\u00a01, pp. 1\u201343. \u00a0https:\/\/doi.org\/10.18637\/jss.v062.i01","journal-title":"J. Stat. Software"},{"key":"7471_CR2","volume-title":"Spatial Time Series: Analysis\u2013Forecasting\u2013Control","author":"R. Bennett","year":"1979","unstructured":"Bennett, R., Spatial Time Series: Analysis\u2013Forecasting\u2013Control, London: Pion, 1979."},{"key":"7471_CR3","volume-title":"Time Series Analysis: Univariate and Multivariate Methods","author":"W.S. Wei","year":"1989","unstructured":"Wei, W.S., Time Series Analysis: Univariate and Multivariate Methods, Reading, Mass.: Addison-Wesley, 1989."},{"key":"7471_CR4","doi-asserted-by":"publisher","unstructured":"Kalpakis, K., Gada, D., and Puttagunta, V., Distance measures for effective clustering of ARIMA time-series, Proc. 2001 IEEE Int. Conf. on Data Mining, San Jose, Calif., 2001, IEEE, 2001, pp. 273\u2013280. \u00a0https:\/\/doi.org\/10.1109\/ICDM.2001.989529","DOI":"10.1109\/ICDM.2001.989529"},{"key":"7471_CR5","unstructured":"Ahonen, T.E., Lemstr\u00f6m, K., and Linkola, S., Compression-based similarity measures in symbolic, polyphonic music, Proc. 12th Int. Society for Music Information Retrieval Conf. (ISMIR 2011), Klapuri, A. and Leider, C., Eds., Miami: Univ. Miami, 2011, pp. 91\u201396."},{"key":"7471_CR6","doi-asserted-by":"publisher","unstructured":"Keogh, S., Lonardi, S., and Ratanamahatana, C.A., Towards parameter-free data mining, Proc. Tenth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Seattle, Wash., 2004, New York: Association for Computing Machinery, 2004, pp. 206\u2013215. \u00a0https:\/\/doi.org\/10.1145\/1014052.1014077","DOI":"10.1145\/1014052.1014077"},{"key":"7471_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s11634-006-0004-6","volume":"1","author":"A.D. Chouakria","year":"2007","unstructured":"Chouakria, A.D. and Nagabhushan, P.N., Adaptive dissimilarity index for measuring time series proximity, Adv. Data Anal. Classification, 2007, vol. 1, no. 1, pp. 5\u201321. \u00a0https:\/\/doi.org\/10.1007\/s11634-006-0004-6","journal-title":"Adv. Data Anal. Classification"},{"key":"7471_CR8","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TASSP.1981.1163527","volume":"29","author":"C. Myers","year":"1981","unstructured":"Myers, C. and Rabiner, L., A level building dynamic time warping algorithm for connected word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1981, vol. 29, no. 2, pp. 284\u2013297. \u00a0https:\/\/doi.org\/10.1109\/TASSP.1981.1163527","journal-title":"IEEE Trans. Acoust., Speech, Signal Process."},{"key":"7471_CR9","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1109\/TASSP.1980.1163491","volume":"28","author":"C. Myers","year":"1980","unstructured":"Myers, C., Rabiner, L., and Rosenberg, A., Performance tradeoffs in dynamic time warping algorithms for isolated word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1980, vol. 28, no. 6, pp. 623\u2013635. \u00a0https:\/\/doi.org\/10.1109\/TASSP.1980.1163491","journal-title":"IEEE Trans. Acoust., Speech, Signal Process."},{"key":"7471_CR10","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1109\/TASSP.1978.1163164","volume":"26","author":"L. Rabiner","year":"1978","unstructured":"Rabiner, L., Rosenberg, A., and Levinson, S., Considerations in dynamic time warping algorithms for discrete word recognition, IEEE Trans. Acoust., Speech, Signal Process., 1978, vol. 26, no. 6, pp. 575\u2013582. \u00a0https:\/\/doi.org\/10.1109\/TASSP.1978.1163164","journal-title":"IEEE Trans. Acoust., Speech, Signal Process."},{"key":"7471_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, N. and Yao, Y., Speaker recognition based on dynamic time warping and Gaussian mixture model, 39th Chinese Control Conf. (CCC), Shenyang, China, 2020, IEEE, 2020, pp.\u00a01174\u20131177. \u00a0https:\/\/doi.org\/10.23919\/CCC50068.2020.9188632","DOI":"10.23919\/CCC50068.2020.9188632"},{"key":"7471_CR12","doi-asserted-by":"publisher","unstructured":"Jing, M., Mac Namee, B., McLaughlin, D., Steele, D., McNamee, S., Cullen, P., Finlay, D., and McLaughlin, J., Enhance categorisation of multilevel high-sensitivity cardiovascular biomarkers from lateral flow immunoassay images via neural networks and dynamic time warping, IEEE Int. Conf. on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, IEEE, 2020, pp. 365\u2013369. https:\/\/doi.org\/10.1109\/ICIP40778.2020.9190827","DOI":"10.1109\/ICIP40778.2020.9190827"},{"key":"7471_CR13","doi-asserted-by":"publisher","first-page":"5535","DOI":"10.1109\/TGRS.2020.3022051","volume":"59","author":"G. Huang","year":"2021","unstructured":"Huang, G., Chen, X., and Chen, Y., P-P and dynamic time warped P-SV wave AVA joint-inversion with l\n               1\u20132 regularization, IEEE Trans. Geosci. Remote Sensing, 2021, vol. 59, no. 7, pp. 5535\u20135548. \u00a0https:\/\/doi.org\/10.1109\/TGRS.2020.3022051","journal-title":"IEEE Trans. Geosci. Remote Sensing"},{"key":"7471_CR14","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1109\/JSEN.2019.2942317","volume":"20","author":"A.S. Lathe","year":"2020","unstructured":"Lathe, A.S. and Gautam, A., Estimating vertical profile irregularities from vehicle dynamics measurements, IEEE Sensors J., 2020, vol. 20, no. 1, pp. 377\u2013385. \u00a0https:\/\/doi.org\/10.1109\/JSEN.2019.2942317","journal-title":"IEEE Sensors J."},{"key":"7471_CR15","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/JSSC.2020.3021066","volume":"56","author":"Z. Chen","year":"2021","unstructured":"Chen, Z. and Gu, J., High-throughput dynamic time warping accelerator for time-series classification with pipelined mixed-signal time-domain computing, IEEE J. Solid-State Circuits, 2021, vol. 56, no. 2, pp. 624\u2013635.\u00a0https:\/\/doi.org\/10.1109\/JSSC.2020.3021066","journal-title":"IEEE J. Solid-State Circuits"},{"key":"7471_CR16","doi-asserted-by":"publisher","first-page":"108359","DOI":"10.1109\/ACCESS.2020.3001379","volume":"8","author":"Y. Si","year":"2020","unstructured":"Si, Y., Chen, Z., Sun, J., Zhang, D., and Qian, P., A data-driven fault detection framework using Mahalanobis distance based dynamic time warping, IEEE Access, 2020, vol. 8, pp.\u00a0108359\u2013108370. \u00a0https:\/\/doi.org\/10.1109\/ACCESS.2020.3001379","journal-title":"IEEE Access"},{"key":"7471_CR17","doi-asserted-by":"publisher","first-page":"29078","DOI":"10.1109\/ACCESS.2018.2839765","volume":"6","author":"H. Wang","year":"2018","unstructured":"Wang, H., Huo, N., Li, J., Wang, K., and Wang, Z., A road quality detection method based on the Mahalanobis\u2013Taguchi system, IEEE Access, 2018, vol. 6, pp. 29078\u201329087. \u00a0https:\/\/doi.org\/10.1109\/ACCESS.2018.2839765","journal-title":"IEEE Access"},{"key":"7471_CR18","doi-asserted-by":"publisher","unstructured":"Geler, Z., Kurbalija, V., Ivanovi\u0107, M., and Radovanovi\u0107, M., Time-series classification with constrained DTW distance and inverse-square weighted k-NN, Int. Conf. on Innovations in Intelligent Systems and Applications (INISTA), Novi Sad, Serbia, 2020, IEEE, 2020, pp.\u00a01\u20137. \u00a0https:\/\/doi.org\/10.1109\/INISTA49547.2020.9194639","DOI":"10.1109\/INISTA49547.2020.9194639"},{"key":"7471_CR19","doi-asserted-by":"publisher","first-page":"3427","DOI":"10.3390\/s19153427","volume":"19","author":"L. Xie","year":"2019","unstructured":"Xie, L., Chen, P., Chen, S., Yu, K., and Sun, H., Low-cost and highly sensitive wearable sensor based on Napkin for health monitoring, Sensors, 2019, vol. 19, no. 15, p. 3427. \u00a0https:\/\/doi.org\/10.3390\/s19153427","journal-title":"Sensors"},{"key":"7471_CR20","unstructured":"Liu, Y., Nie, L., Han, L., Zhang, L., and Rosenblum, D.S., Action2Activity: Recognizing complex activities from sensor data, Proc. Twenty-Fourth Int. Joint Conf. on Artificial Intelligence (IJCAI 2015), Buenos Aires, 2015, AAAI Press, 2015, pp. 1617\u20131623."},{"key":"7471_CR21","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.neucom.2015.08.096","volume":"181","author":"Y. Liu","year":"2016","unstructured":"Liu, Y., Nie, L., Liu, L., and Rosenblum, D.S., From action to activity: Sensor-based activity recognition, Neurocomputing, 2016, vol. 181, pp. 108\u2013115. \u00a0https:\/\/doi.org\/10.1016\/j.neucom.2015.08.096","journal-title":"Neurocomputing"},{"key":"7471_CR22","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSEN.2011.2111453","volume":"12","author":"M.Z.U. Rahman","year":"2012","unstructured":"Rahman, M.Z.U., Shaik, R.A., and Reddy, D.V.R.K., Efficient and simplified adaptive noise cancelers for ECG sensor based remote health monitoring, IEEE Sensors J., 2012, vol.\u00a012, no. 3, pp. 566\u2013573. \u00a0https:\/\/doi.org\/10.1109\/JSEN.2011.2111453","journal-title":"IEEE Sensors J."},{"key":"7471_CR23","doi-asserted-by":"publisher","unstructured":"Echoda, N.J.A., Farooq, S.Z., Xuebao, H., Chukwuma, J., and Yang, D., Multipath mitigation analysis using Hatch filter and DTW in single frequency RTK, IEEE Int. Conf. on Artificial Intelligence and Information Systems (ICAAIS), Dalian, China, 2020, IEEE, 2020, pp.\u00a0273\u2013277. \u00a0https:\/\/doi.org\/10.1109\/ICAIIS49377.2020.9194831","DOI":"10.1109\/ICAIIS49377.2020.9194831"},{"key":"7471_CR24","doi-asserted-by":"publisher","first-page":"156634","DOI":"10.1109\/ACCESS.2020.3009679","volume":"8","author":"F. Hong","year":"2020","unstructured":"Hong, F., Chen, J., Zhang, Z., Wang, R., and Gao, M., Time series risk prediction based on LSTM and a variant DTW algorithm: Application of bed inventory overturn prevention in a\u00a0pant-leg CFB boiler, IEEE Access, 2020, vol. 8, pp. 156634\u2013156644. \u00a0https:\/\/doi.org\/10.1109\/ACCESS.2020.3009679","journal-title":"IEEE Access"},{"key":"7471_CR25","doi-asserted-by":"publisher","unstructured":"Ashouri, A., Hu, Y., Newsham, G.R., and Shen, W., Energy performance based anomaly detection in non-residential buildings using symbolic aggregate approximation, IEEE 14th Int. Conf. on Automation Science and Engineering (CASE), Munich, 2018, IEEE, 2018, pp.\u00a01400\u20131405. \u00a0https:\/\/doi.org\/10.1109\/COASE.2018.8560433","DOI":"10.1109\/COASE.2018.8560433"},{"key":"7471_CR26","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1109\/JSEN.2019.2952857","volume":"20","author":"A. Basavaraju","year":"2020","unstructured":"Basavaraju, A., Du, J., Zhou, F., and Ji, J., A machine learning approach to road surface anomaly assessment using smartphone sensors, IEEE Sensors J., 2020, vol. 20, no. 5, pp. 2635\u20132647. \u00a0https:\/\/doi.org\/10.1109\/JSEN.2019.2952857","journal-title":"IEEE Sensors J."},{"key":"7471_CR27","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","volume":"44","author":"Y.-S. Jeong","year":"2011","unstructured":"Jeong, Y.-S., Jeong, M.K., and Omitaomu, O.A., Weighted dynamic time warping for time series classification, Pattern Recognit., 2011, vol. 44, no. 9, pp. 2231\u20132240. \u00a0https:\/\/doi.org\/10.1016\/j.patcog.2010.09.022","journal-title":"Pattern Recognit."},{"key":"7471_CR28","doi-asserted-by":"publisher","unstructured":"Wang, L., Wang, B., Han, Z., Zhang, Z., Kong, F., and Wang, L., Research of the hybrid tire pressure monitoring system, DEStech Trans. Eng. Technol. Res., 2017, pp. 191\u2013194. \u00a0https:\/\/doi.org\/10.12783\/dtetr\/mime2016\/10230","DOI":"10.12783\/dtetr\/mime2016\/10230"},{"key":"7471_CR29","doi-asserted-by":"publisher","unstructured":"Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., and Selavo, L., Real time pothole detection using Android smartphones with accelerometers, Int. Conf. on Distributed Computing in Sensor Systems and Workshops (DCOSS), Barcelona, 2011, IEEE, 2011, pp. 1\u20136. \u00a0https:\/\/doi.org\/10.1109\/DCOSS.2011.5982206","DOI":"10.1109\/DCOSS.2011.5982206"}],"container-title":["Automatic Control and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3103\/S0146411622020067.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.3103\/S0146411622020067","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3103\/S0146411622020067.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T22:04:20Z","timestamp":1773612260000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.3103\/S0146411622020067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["7471"],"URL":"https:\/\/doi.org\/10.3103\/s0146411622020067","relation":{},"ISSN":["0146-4116","1558-108X"],"issn-type":[{"value":"0146-4116","type":"print"},{"value":"1558-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4]]},"assertion":[{"value":"2 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"CONFLICT OF INTEREST"}}]}}