Auswahl der wissenschaftlichen Literatur zum Thema „Time series search“
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Zeitschriftenartikel zum Thema "Time series search"
Folgado, Duarte, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz und Hugo Gamboa. „TSSEARCH: Time Series Subsequence Search Library“. SoftwareX 18 (Juni 2022): 101049. http://dx.doi.org/10.1016/j.softx.2022.101049.
Der volle Inhalt der QuelleLuu, Do Ngoc, Nguyen Ngoc Phien und Duong Tuan Anh. „Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search“. International Journal of Machine Learning and Computing 11, Nr. 4 (August 2021): 274–80. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1047.
Der volle Inhalt der QuellePRATT, KEVIN B., und EUGENE FINK. „SEARCH FOR PATTERNS IN COMPRESSED TIME SERIES“. International Journal of Image and Graphics 02, Nr. 01 (Januar 2002): 89–106. http://dx.doi.org/10.1142/s0219467802000482.
Der volle Inhalt der QuelleSHIN, MIN-SU, und YONG-IK BYUN. „EFFICIENT PERIOD SEARCH FOR TIME SERIES PHOTOMETRY“. Journal of The Korean Astronomical Society 37, Nr. 2 (01.06.2004): 79–85. http://dx.doi.org/10.5303/jkas.2004.37.2.079.
Der volle Inhalt der QuelleIbrahim, Ibrahim A., und Abdullah M. Albarrak. „Correlation-based search for time series data“. International Journal of Computer Applications in Technology 62, Nr. 2 (2020): 158. http://dx.doi.org/10.1504/ijcat.2020.10026419.
Der volle Inhalt der QuelleIbrahim, A., und Abdullah M. Albarrak. „Correlation-based search for time series data“. International Journal of Computer Applications in Technology 62, Nr. 2 (2020): 158. http://dx.doi.org/10.1504/ijcat.2020.104684.
Der volle Inhalt der QuelleLuo, Wei, Marcus Gallagher und Janet Wiles. „Parameter-Free Search of Time-Series Discord“. Journal of Computer Science and Technology 28, Nr. 2 (März 2013): 300–310. http://dx.doi.org/10.1007/s11390-013-1330-8.
Der volle Inhalt der QuelleHuang, Silu, Erkang Zhu, Surajit Chaudhuri und Leonhard Spiegelberg. „T-Rex: Optimizing Pattern Search on Time Series“. Proceedings of the ACM on Management of Data 1, Nr. 2 (13.06.2023): 1–26. http://dx.doi.org/10.1145/3589275.
Der volle Inhalt der QuelleXiaoling WANG, und Clement H. C. LEUNG. „Representing Image Search Performance Using Time Series Models“. International Journal of Advancements in Computing Technology 2, Nr. 4 (31.10.2010): 140–50. http://dx.doi.org/10.4156/ijact.vol2.issue4.15.
Der volle Inhalt der QuelleLiabotis, Ioannis, Babis Theodoulidis und Mohamad Saraaee. „Improving Similarity Search in Time Series Using Wavelets“. International Journal of Data Warehousing and Mining 2, Nr. 2 (April 2006): 55–81. http://dx.doi.org/10.4018/jdwm.2006040103.
Der volle Inhalt der QuelleDissertationen zum Thema "Time series search"
Barsk, Viktor. „Time Series Search Using Traits“. Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-128580.
Der volle Inhalt der QuelleXia, Betty Bin. „Similarity search in time series data sets“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24275.pdf.
Der volle Inhalt der QuelleBodwick, M. K. „Multivariate time series : The search for structure“. Thesis, Lancaster University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233978.
Der volle Inhalt der QuelleAhsan, Ramoza. „Time Series Data Analytics“. Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.
Der volle Inhalt der QuelleBardwell, Lawrence. „Efficient search methods for high dimensional time-series“. Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/89685/.
Der volle Inhalt der QuelleSchäfer, Patrick. „Scalable time series similarity search for data analytics“. Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17338.
Der volle Inhalt der QuelleA time series is a collection of values sequentially recorded from sensors or live observations over time. Sensors for recording time series have become cheap and omnipresent. While data volumes explode, research in the field of time series data analytics has focused on the availability of (a) pre-processed and (b) moderately sized time series datasets in the last decades. The analysis of real world datasets raises two major problems: Firstly, state-of-the-art similarity models require the time series to be pre-processed. Pre-processing aims at extracting approximately aligned characteristic subsequences and reducing noise. It is typically performed by a domain expert, may be more time consuming than the data mining part itself, and simply does not scale to large data volumes. Secondly, time series research has been driven by accuracy metrics and not by reasonable execution times for large data volumes. This results in quadratic to biquadratic computational complexities of state-of-the-art similarity models. This dissertation addresses both issues by introducing a symbolic time series representation and three different similarity models. These contribute to state of the art by being pre-processing-free, noise-robust, and scalable. Our experimental evaluation on 91 real-world and benchmark datasets shows that our methods provide higher accuracy for most datasets when compared to 15 state-of-the-art similarity models. Meanwhile they are up to three orders of magnitude faster, require less pre-processing for noise or alignment, or scale to large data volumes.
Mitchell, F. „Painless knowledge acquisition for time series data“. Thesis, University of Aberdeen, 1997. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU100889.
Der volle Inhalt der QuelleCharapko, Aleksey. „Time Series Similarity Search in Distributed Key-Value Data Stores Using R-Trees“. UNF Digital Commons, 2015. http://digitalcommons.unf.edu/etd/565.
Der volle Inhalt der QuelleMuhammad, Fuad Muhammad Marwan. „Similarity Search in High-dimensional Spaces with Applications to Time Series Data Mining and Information Retrieval“. Phd thesis, Université de Bretagne Sud, 2011. http://tel.archives-ouvertes.fr/tel-00619953.
Der volle Inhalt der QuelleSchäfer, Patrick [Verfasser], Alexander [Akademischer Betreuer] Reinefeld, Ulf [Akademischer Betreuer] Leser und Artur [Akademischer Betreuer] Andrzejak. „Scalable time series similarity search for data analytics / Patrick Schäfer. Gutachter: Alexander Reinefeld ; Ulf Leser ; Artur Andrzejak“. Berlin : Mathematisch-Naturwissenschaftliche Fakultät, 2015. http://d-nb.info/1078309620/34.
Der volle Inhalt der QuelleBücher zum Thema "Time series search"
Fuentes, Andreas. The determinants of on-the-job search: A time series analysis for Britain. Oxford: Oxford University, Institute of Economics and Statistics, 1998.
Den vollen Inhalt der Quelle findenPerotti, Roberto. In search of the transmission mechanism of fiscal policy. Cambridge, Mass: National Bureau of Economic Research, 2007.
Den vollen Inhalt der Quelle findenMarie, Robertson Eleanor. The search. New York, N.Y: G.P. Putnam's Sons, 2010.
Den vollen Inhalt der Quelle findenMarie, Robertson Eleanor. The search. New York: G.P. Putnam's Sons, 2010.
Den vollen Inhalt der Quelle findenMarie, Robertson Eleanor. The Search. New York: Penguin USA, Inc., 2010.
Den vollen Inhalt der Quelle findenMarie, Robertson Eleanor. Opasnyĭ sled. Moskva: Ėksmo, 2011.
Den vollen Inhalt der Quelle findenMarie, Robertson Eleanor. De zoektocht. Amsterdam: Boekerij, 2013.
Den vollen Inhalt der Quelle findenLarson, Erik. The devil in the white city: Murder, magic, and madness at the fair that changed America. New York: Crown Publishers, 2003.
Den vollen Inhalt der Quelle findenLarson, Erik. The devil in the white city: Murder, magic, and madness at the fair that changed America. Waterville, Me: Thorndike Press, 2003.
Den vollen Inhalt der Quelle findenLarson, Erik. Bai cheng e mo. 8. Aufl. Beijing Shi: Ren min wen xue chu ban she, 2010.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Time series search"
Schwarzenberg-Czerny, Alex. „Period Search“. In Astronomical Time Series, 183–86. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8941-3_19.
Der volle Inhalt der QuelleSperandio, Ricardo Carlini, Simon Malinowski, Laurent Amsaleg und Romain Tavenard. „Time Series Retrieval Using DTW-Preserving Shapelets“. In Similarity Search and Applications, 257–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02224-2_20.
Der volle Inhalt der QuelleShasha, Dennis, und Yunyue Zhu. „Flexible Similarity Search“. In High Performance Discovery in Time Series, 87–100. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4046-2_4.
Der volle Inhalt der QuelleKaramitopoulos, Leonidas, Georgios Evangelidis und Dimitris Dervos. „PCA-based Time Series Similarity Search“. In Annals of Information Systems, 255–76. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-1280-0_11.
Der volle Inhalt der QuelleAßfalg, Johannes, Hans-Peter Kriegel, Peer Kröger und Matthias Renz. „Probabilistic Similarity Search for Uncertain Time Series“. In Lecture Notes in Computer Science, 435–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02279-1_31.
Der volle Inhalt der QuelleKashyap, Shrikant, Mong Li Lee und Wynne Hsu. „Similar Subsequence Search in Time Series Databases“. In Lecture Notes in Computer Science, 232–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23088-2_16.
Der volle Inhalt der QuelleSanchez, Heider, und Benjamin Bustos. „Anomaly Detection in Streaming Time Series Based on Bounding Boxes“. In Similarity Search and Applications, 201–13. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11988-5_19.
Der volle Inhalt der QuelleMovchan, Aleksandr, und Mikhail Zymbler. „Time Series Subsequence Similarity Search Under Dynamic Time Warping Distance on the Intel Many-core Accelerators“. In Similarity Search and Applications, 295–306. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25087-8_28.
Der volle Inhalt der Quellevon Landesberger, Tatiana, Viktor Voss und Jörn Kohlhammer. „Semantic Search and Visualization of Time-Series Data“. In Studies in Computational Intelligence, 205–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02184-8_14.
Der volle Inhalt der QuelleChoy, Murphy, und Ma Nang Laik. „Intelligent Time Series Forecasting Through Neighbourhood Search Heuristics“. In Advances in Intelligent Systems and Computing, 434–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03405-4_30.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Time series search"
Buono, Paolo, Aleks Aris, Catherine Plaisant, Amir Khella und Ben Shneiderman. „Interactive pattern search in time series“. In Electronic Imaging 2005, herausgegeben von Robert F. Erbacher, Jonathan C. Roberts, Matti T. Grohn und Katy Borner. SPIE, 2005. http://dx.doi.org/10.1117/12.587537.
Der volle Inhalt der QuelleHsieh, Tsung-Yu, Suhang Wang, Yiwei Sun und Vasant Honavar. „Explainable Multivariate Time Series Classification“. In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441815.
Der volle Inhalt der QuelleRakhshani, Hojjat, Hassan Ismail Fawaz, Lhassane Idoumghar, Germain Forestier, Julien Lepagnot, Jonathan Weber, Mathieu Brevilliers und Pierre-Alain Muller. „Neural Architecture Search for Time Series Classification“. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206721.
Der volle Inhalt der QuelleKaramitopoulos, Leonidas, und Georgios Evangelidis. „Cluster-Based Similarity Search in Time Series“. In 2009 Fourth Balkan Conference in Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bci.2009.22.
Der volle Inhalt der QuelleCharisi, Amalia, Fragkiskos D. Malliaros, Evangelia I. Zacharaki und Vasileios Megalooikonomou. „Multiresolution similarity search in time series data“. In the 6th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2504335.2504370.
Der volle Inhalt der QuellePeng, Jinglin, Hongzhi Wang, Jianzhong Li und Hong Gao. „Set-based Similarity Search for Time Series“. In SIGMOD/PODS'16: International Conference on Management of Data. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2882903.2882963.
Der volle Inhalt der QuelleZhang, Chuanlei, Ji'an Luo, Shanwen Zhang und Chen Zhang. „Introduction to time series search engine systems“. In 2012 International Conference on Systems and Informatics (ICSAI). IEEE, 2012. http://dx.doi.org/10.1109/icsai.2012.6223532.
Der volle Inhalt der QuelleWang, Shiyu. „NeuralReconciler for Hierarchical Time Series Forecasting“. In WSDM '24: The 17th ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3616855.3635806.
Der volle Inhalt der QuelleWi, Hyowon, Yehjin Shin und Noseong Park. „Continuous-time Autoencoders for Regular and Irregular Time Series Imputation“. In WSDM '24: The 17th ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3616855.3635831.
Der volle Inhalt der QuelleHayran, Ahmet, Hasan Ogul und Esma Ozkoc. „Content-Based Search on Time-Series Microarray Databases“. In 2014 25th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2014. http://dx.doi.org/10.1109/dexa.2014.33.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Time series search"
Audoly, Richard. Firm Dynamics and Random Search over the Business Cycle. Federal Reserve Bank of New York, August 2023. http://dx.doi.org/10.59576/sr.1069.
Der volle Inhalt der QuelleRosen, Michael, C. Matthew Stewart, Hadi Kharrazi, Ritu Sharma, Montrell Vass, Allen Zhang und Eric B. Bass. Potential Harms Resulting From Patient-Clinician Real-Time Clinical Encounters Using Video-based Telehealth: A Rapid Evidence Review. Agency for Healthcare Research and Quality (AHRQ), September 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4telehealth.
Der volle Inhalt der QuelleParsons, Helen M., Hamdi I. Abdi, Victoria A. Nelson, Amy M. Claussen, Brittin L. Wagner, Karim T. Sadak, Peter B. Scal, Timothy J. Wilt und Mary Butler. Transitions of Care From Pediatric to Adult Services for Children With Special Healthcare Needs. Agency for Healthcare Research and Quality (AHRQ), Mai 2022. http://dx.doi.org/10.23970/ahrqepccer255.
Der volle Inhalt der QuelleJohnson, Eric M., Robert Urquhart und Maggie O'Neil. The Importance of Geospatial Data to Labor Market Information. RTI Press, Juni 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0017.1806.
Der volle Inhalt der QuelleQuak, Evert-Jan. K4D’s Work on the Indirect Impacts of COVID-19 in Low- and Middle- Income Countries. Institute of Development Studies (IDS), Juni 2021. http://dx.doi.org/10.19088/k4d.2021.093.
Der volle Inhalt der QuelleF, Verdugo-Paiva, Acuña María Paz, Solá Iván und Rada Gabriel. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, September 2023. http://dx.doi.org/10.30846/ies.527e413d283.p1.
Der volle Inhalt der QuelleF, Verdugo-Paiva, Acuña María Paz, Solá Iván und Rada Gabriel. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, September 2023. http://dx.doi.org/10.30846/ies.527e413d282.v1.
Der volle Inhalt der QuelleF, Verdugo-Paiva, Acuña M, Solá I und Rada G. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, September 2023. http://dx.doi.org/10.30846/ies.527e413d28.v1.
Der volle Inhalt der QuelleF, Verdugo-Paiva, Acuña M, Solá I und Rada G. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, September 2023. http://dx.doi.org/10.30846/ies.527e413d28.
Der volle Inhalt der QuelleKellerLynn, Katie. Redwood National and State Parks: Geologic resources inventory report. National Park Service, Oktober 2021. http://dx.doi.org/10.36967/nrr-2287676.
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