Zobrazeno 1 - 10
of 66
pro vyhledávání: '"unevenly spaced time series"'
Autor:
Aida Boudhaouia, Patrice Wira
Publikováno v:
Forecasting, Vol 3, Iss 4, Pp 682-694 (2021)
This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consu
Externí odkaz:
https://doaj.org/article/d3ee0d17b53640418c1416648408b3f5
Autor:
Vargas-Alemañy, Juan A.
This thesis, structured in two parts, addresses a series of problems of relevance in the field of Spatial Geodesy. The first part delves into the application of satellite gravity data to enhance our understanding of water transport dynamics. Here, we
Externí odkaz:
http://hdl.handle.net/10045/141070
Akademický článek
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Autor:
Sinander, Pierre, Ahmed, Asik
This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissim
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-340421
Autor:
Mihael Brenčič
Publikováno v:
Geologija, Vol 52, Iss 2, Pp 165-174 (2009)
Statistical analyses of calcimetric data from boreholes BV-1 (north of PodpeČ) and BV-2 (south of ^rna vas) on Ljubljansko barje in central Slovenia are given. The original data are represented as unevenly spaced time series that are translated into
Externí odkaz:
https://doaj.org/article/e1389ca169f248b6a3f80bccceb502e4
Autor:
Janusz Jezewski, Michal Jezewski, Tomasz Kupka, Adam Matonia, Janusz Wrobel, Krzysztof Horoba
Publikováno v:
Biocybernetics and Biomedical Engineering. 40:388-403
The most commonly used method of fetal monitoring is based on analysis of the fetal heart activity. Computer-aided fetal monitoring enables extraction of information hidden for visual interpretation – the instantaneous fetal heart rate (FHR) variab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d33ca353c319f886df35282cf6734b02
https://doi.org/10.1016/j.bbe.2019.12.005
https://doi.org/10.1016/j.bbe.2019.12.005
Publikováno v:
Nauka i Tehnika, Vol 18, Iss 6, Pp 519-524 (2019)
Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76b4e39113e5fe5092570393ab14336d
https://doi.org/10.21122/2227-1031-2019-18-6-519-524
https://doi.org/10.21122/2227-1031-2019-18-6-519-524
Publikováno v:
IJCNN
In high dimensional tensorial time series prediction, it is highly desired to preserve spatial structural and underlying continuous sequential information in modelling. Existing methods either destroy the spatial structure and require a large number
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c39c736e0209e49d308117ac45ee426f
https://doi.org/10.1109/ijcnn52387.2021.9533771
https://doi.org/10.1109/ijcnn52387.2021.9533771
Publikováno v:
IEEE Internet of Things Journal
Sørensen, R B, Nielsen, J J & Popovski, P 2020, ' Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks ', IEEE Internet of Things Journal, vol. 7, no. 8, 9046825, pp. 7368-7376 . https://doi.org/10.1109/JIOT.2020.2983217
Sørensen, R B, Nielsen, J J & Popovski, P 2020, ' Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks ', IEEE Internet of Things Journal, vol. 7, no. 8, 9046825, pp. 7368-7376 . https://doi.org/10.1109/JIOT.2020.2983217
One of the central problems in massive Internet-of-Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermitt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::953851a6776a59695e50b6468d43fef1
Publikováno v:
Bayesian Anal. 14, no. 4 (2019), 1173-1199
We present an integrated open population model where the population dynamics are defined by a differential equation, and the related statistical model utilizes a Poisson binomial convolution likelihood. Key advantages of the proposed approach over ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::373cdc458565e6e2823270e77597ced0
https://doi.org/10.1214/19-ba1152
https://doi.org/10.1214/19-ba1152