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Artykuły w czasopismach na temat "Time series search"
Folgado, Duarte, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz i Hugo Gamboa. "TSSEARCH: Time Series Subsequence Search Library". SoftwareX 18 (czerwiec 2022): 101049. http://dx.doi.org/10.1016/j.softx.2022.101049.
Pełny tekst źródłaLuu, Do Ngoc, Nguyen Ngoc Phien i 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 (sierpień 2021): 274–80. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1047.
Pełny tekst źródłaPRATT, KEVIN B., i EUGENE FINK. "SEARCH FOR PATTERNS IN COMPRESSED TIME SERIES". International Journal of Image and Graphics 02, nr 01 (styczeń 2002): 89–106. http://dx.doi.org/10.1142/s0219467802000482.
Pełny tekst źródłaSHIN, MIN-SU, i YONG-IK BYUN. "EFFICIENT PERIOD SEARCH FOR TIME SERIES PHOTOMETRY". Journal of The Korean Astronomical Society 37, nr 2 (1.06.2004): 79–85. http://dx.doi.org/10.5303/jkas.2004.37.2.079.
Pełny tekst źródłaIbrahim, Ibrahim A., i 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.
Pełny tekst źródłaIbrahim, A., i 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.
Pełny tekst źródłaLuo, Wei, Marcus Gallagher i Janet Wiles. "Parameter-Free Search of Time-Series Discord". Journal of Computer Science and Technology 28, nr 2 (marzec 2013): 300–310. http://dx.doi.org/10.1007/s11390-013-1330-8.
Pełny tekst źródłaHuang, Silu, Erkang Zhu, Surajit Chaudhuri i 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.
Pełny tekst źródłaXiaoling WANG, i 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.
Pełny tekst źródłaLiabotis, Ioannis, Babis Theodoulidis i Mohamad Saraaee. "Improving Similarity Search in Time Series Using Wavelets". International Journal of Data Warehousing and Mining 2, nr 2 (kwiecień 2006): 55–81. http://dx.doi.org/10.4018/jdwm.2006040103.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaXia, 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.
Pełny tekst źródłaBodwick, M. K. "Multivariate time series : The search for structure". Thesis, Lancaster University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233978.
Pełny tekst źródłaAhsan, Ramoza. "Time Series Data Analytics". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.
Pełny tekst źródłaBardwell, Lawrence. "Efficient search methods for high dimensional time-series". Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/89685/.
Pełny tekst źródłaSchäfer, 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.
Pełny tekst źródłaA 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.
Pełny tekst źródłaCharapko, Aleksey. "Time Series Similarity Search in Distributed Key-Value Data Stores Using R-Trees". UNF Digital Commons, 2015. http://digitalcommons.unf.edu/etd/565.
Pełny tekst źródłaMuhammad, 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.
Pełny tekst źródłaSchäfer, Patrick [Verfasser], Alexander [Akademischer Betreuer] Reinefeld, Ulf [Akademischer Betreuer] Leser i 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.
Pełny tekst źródłaKsiążki na temat "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.
Znajdź pełny tekst źródłaPerotti, Roberto. In search of the transmission mechanism of fiscal policy. Cambridge, Mass: National Bureau of Economic Research, 2007.
Znajdź pełny tekst źródłaMarie, Robertson Eleanor. The search. New York, N.Y: G.P. Putnam's Sons, 2010.
Znajdź pełny tekst źródłaMarie, Robertson Eleanor. The search. New York: G.P. Putnam's Sons, 2010.
Znajdź pełny tekst źródłaMarie, Robertson Eleanor. The Search. New York: Penguin USA, Inc., 2010.
Znajdź pełny tekst źródłaMarie, Robertson Eleanor. Opasnyĭ sled. Moskva: Ėksmo, 2011.
Znajdź pełny tekst źródłaMarie, Robertson Eleanor. De zoektocht. Amsterdam: Boekerij, 2013.
Znajdź pełny tekst źródłaLarson, Erik. The devil in the white city: Murder, magic, and madness at the fair that changed America. New York: Crown Publishers, 2003.
Znajdź pełny tekst źródłaLarson, Erik. The devil in the white city: Murder, magic, and madness at the fair that changed America. Waterville, Me: Thorndike Press, 2003.
Znajdź pełny tekst źródłaLarson, Erik. Bai cheng e mo. Wyd. 8. Beijing Shi: Ren min wen xue chu ban she, 2010.
Znajdź pełny tekst źródłaCzęści książek na temat "Time series search"
Schwarzenberg-Czerny, Alex. "Period Search". W Astronomical Time Series, 183–86. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8941-3_19.
Pełny tekst źródłaSperandio, Ricardo Carlini, Simon Malinowski, Laurent Amsaleg i Romain Tavenard. "Time Series Retrieval Using DTW-Preserving Shapelets". W Similarity Search and Applications, 257–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02224-2_20.
Pełny tekst źródłaShasha, Dennis, i Yunyue Zhu. "Flexible Similarity Search". W 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.
Pełny tekst źródłaKaramitopoulos, Leonidas, Georgios Evangelidis i Dimitris Dervos. "PCA-based Time Series Similarity Search". W Annals of Information Systems, 255–76. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-1280-0_11.
Pełny tekst źródłaAßfalg, Johannes, Hans-Peter Kriegel, Peer Kröger i Matthias Renz. "Probabilistic Similarity Search for Uncertain Time Series". W 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.
Pełny tekst źródłaKashyap, Shrikant, Mong Li Lee i Wynne Hsu. "Similar Subsequence Search in Time Series Databases". W 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.
Pełny tekst źródłaSanchez, Heider, i Benjamin Bustos. "Anomaly Detection in Streaming Time Series Based on Bounding Boxes". W Similarity Search and Applications, 201–13. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11988-5_19.
Pełny tekst źródłaMovchan, Aleksandr, i Mikhail Zymbler. "Time Series Subsequence Similarity Search Under Dynamic Time Warping Distance on the Intel Many-core Accelerators". W Similarity Search and Applications, 295–306. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25087-8_28.
Pełny tekst źródłavon Landesberger, Tatiana, Viktor Voss i Jörn Kohlhammer. "Semantic Search and Visualization of Time-Series Data". W Studies in Computational Intelligence, 205–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02184-8_14.
Pełny tekst źródłaChoy, Murphy, i Ma Nang Laik. "Intelligent Time Series Forecasting Through Neighbourhood Search Heuristics". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Time series search"
Buono, Paolo, Aleks Aris, Catherine Plaisant, Amir Khella i Ben Shneiderman. "Interactive pattern search in time series". W Electronic Imaging 2005, redaktorzy Robert F. Erbacher, Jonathan C. Roberts, Matti T. Grohn i Katy Borner. SPIE, 2005. http://dx.doi.org/10.1117/12.587537.
Pełny tekst źródłaHsieh, Tsung-Yu, Suhang Wang, Yiwei Sun i Vasant Honavar. "Explainable Multivariate Time Series Classification". W 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.
Pełny tekst źródłaRakhshani, Hojjat, Hassan Ismail Fawaz, Lhassane Idoumghar, Germain Forestier, Julien Lepagnot, Jonathan Weber, Mathieu Brevilliers i Pierre-Alain Muller. "Neural Architecture Search for Time Series Classification". W 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206721.
Pełny tekst źródłaKaramitopoulos, Leonidas, i Georgios Evangelidis. "Cluster-Based Similarity Search in Time Series". W 2009 Fourth Balkan Conference in Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bci.2009.22.
Pełny tekst źródłaCharisi, Amalia, Fragkiskos D. Malliaros, Evangelia I. Zacharaki i Vasileios Megalooikonomou. "Multiresolution similarity search in time series data". W the 6th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2504335.2504370.
Pełny tekst źródłaPeng, Jinglin, Hongzhi Wang, Jianzhong Li i Hong Gao. "Set-based Similarity Search for Time Series". W SIGMOD/PODS'16: International Conference on Management of Data. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2882903.2882963.
Pełny tekst źródłaZhang, Chuanlei, Ji'an Luo, Shanwen Zhang i Chen Zhang. "Introduction to time series search engine systems". W 2012 International Conference on Systems and Informatics (ICSAI). IEEE, 2012. http://dx.doi.org/10.1109/icsai.2012.6223532.
Pełny tekst źródłaWang, Shiyu. "NeuralReconciler for Hierarchical Time Series Forecasting". W 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.
Pełny tekst źródłaWi, Hyowon, Yehjin Shin i Noseong Park. "Continuous-time Autoencoders for Regular and Irregular Time Series Imputation". W 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.
Pełny tekst źródłaHayran, Ahmet, Hasan Ogul i Esma Ozkoc. "Content-Based Search on Time-Series Microarray Databases". W 2014 25th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2014. http://dx.doi.org/10.1109/dexa.2014.33.
Pełny tekst źródłaRaporty organizacyjne na temat "Time series search"
Audoly, Richard. Firm Dynamics and Random Search over the Business Cycle. Federal Reserve Bank of New York, sierpień 2023. http://dx.doi.org/10.59576/sr.1069.
Pełny tekst źródłaRosen, Michael, C. Matthew Stewart, Hadi Kharrazi, Ritu Sharma, Montrell Vass, Allen Zhang i 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), wrzesień 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4telehealth.
Pełny tekst źródłaParsons, Helen M., Hamdi I. Abdi, Victoria A. Nelson, Amy M. Claussen, Brittin L. Wagner, Karim T. Sadak, Peter B. Scal, Timothy J. Wilt i Mary Butler. Transitions of Care From Pediatric to Adult Services for Children With Special Healthcare Needs. Agency for Healthcare Research and Quality (AHRQ), maj 2022. http://dx.doi.org/10.23970/ahrqepccer255.
Pełny tekst źródłaJohnson, Eric M., Robert Urquhart i Maggie O'Neil. The Importance of Geospatial Data to Labor Market Information. RTI Press, czerwiec 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0017.1806.
Pełny tekst źródłaQuak, Evert-Jan. K4D’s Work on the Indirect Impacts of COVID-19 in Low- and Middle- Income Countries. Institute of Development Studies (IDS), czerwiec 2021. http://dx.doi.org/10.19088/k4d.2021.093.
Pełny tekst źródłaF, Verdugo-Paiva, Acuña María Paz, Solá Iván i Rada Gabriel. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, wrzesień 2023. http://dx.doi.org/10.30846/ies.527e413d283.p1.
Pełny tekst źródłaF, Verdugo-Paiva, Acuña María Paz, Solá Iván i Rada Gabriel. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, wrzesień 2023. http://dx.doi.org/10.30846/ies.527e413d282.v1.
Pełny tekst źródłaF, Verdugo-Paiva, Acuña M, Solá I i Rada G. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, wrzesień 2023. http://dx.doi.org/10.30846/ies.527e413d28.v1.
Pełny tekst źródłaF, Verdugo-Paiva, Acuña M, Solá I i Rada G. Is remdesivir an effective intervention in people with acute COVID-19? Epistemonikos Interactive Evidence Synthesis, wrzesień 2023. http://dx.doi.org/10.30846/ies.527e413d28.
Pełny tekst źródłaKellerLynn, Katie. Redwood National and State Parks: Geologic resources inventory report. National Park Service, październik 2021. http://dx.doi.org/10.36967/nrr-2287676.
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