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Tesis sobre el tema "Time series dataset"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 11 de febrero de 2022

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1

Biswas, Debashis. "An Algorithm for Mining Adverse-Event Datasets for Detection of Post Safety Concern of a Drug." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_theses/17.

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Signal detection from Adverse Event Reports (AERs) is important for identifying and analysing drug safety concern after a drug has been released into the market. A safety signal is defined as a possible causal relation between an adverse event and a drug. There are a number of safety signal detection algorithms available for detecting drug safety concern. They compare the ratio of observed count to expected count to find instances of disproportionate reportings of an event for a drug or combination of events for a drug. In this thesis, we present an algorithm to mine the AERs to identify drugs

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2

Abualhamayl, Abdullah Jameel Mr. "APPLY DATA CLUSTERING TO GENE EXPRESSION DATA." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/259.

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Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the dimensionality of the data, and the change of data over time. The genetic groups which are biologically interlinked can be identified through clustering. This project aims to clarify the steps to apply clustering analysis of genes involved in a published dataset. The methodology for this project includes the select

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3

Tihlaříková, Jana. "Statistické vyhodnocení přijímacích zkoušek." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222848.

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This master’s thesis deals with statistical evaluation of the entrance exams at the Faculty of Business and Management of Brno University of Technology, especially the evaluation of the quality of applicants of bachelor study branch “Tax Advisory“. The thesis also includes the forecast of number of applicants, who will apply for the Faculty of Business and Management of Brno University of Technology in the future.

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4

Matsubara, Yasuko. "Statistical Data Mining for Time-series Datasets." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/157475.

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5

Lei, Jiahuan. "An extended BIRCH-based clustering algorithm for large time-series datasets." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29858.

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Temporal data analysis and mining has attracted substantial interest due to theproliferation and ubiquity of time series in many fields. Time series clustering isone of the most popular mining methods, and many time series clustering algorithmsprimarily focus on detecting the clusters in a batch fashion that will use alot of memory space and thus limit the scalability and capability for large timeseries.The BIRCH algorithm has been proven to scale well to large datasets,which is characterized by an incrementally clustering data objects using a singlescan. However the Euclidean distance metric

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6

Oliveira, Guilherme do Nascimento. "Ordered stacks of time series for exploratory analysis of large spatio-temporal datasets." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/130557.

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O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbano

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7

Zeng, Jianfeng. "Time Series Forecasting using Temporal Regularized Matrix Factorization and Its Application to Traffic Speed Datasets." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617109307510099.

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8

Oluwole, Oluwadamilola. "Weather-sensitive, spatially-disaggregated electricity demand model for Nigeria." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33043.

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The historical underinvestment in power infrastructure and the poor performance of power delivery has resulted in extensive and regular power shortages in Nigeria. As Nigeria aims to bridge its power supply gap, the recent deregulation of its electricity market has seen the privatisation of its generation and distribution companies. Ambitious plans have also been put in place to expand the transmission network and the total power generation capacity. However, these plans have been developed with essentially arbitrary estimates for prevailing demand levels as the network and generation limits m

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9

Klinkert, Rickard. "Uncertainty Analysis of Long Term Correction Methods for Annual Average Winds." Thesis, Umeå universitet, Institutionen för fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-59690.

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For the construction of a wind farm, one needs to assess the wind resources of the considered site location. Using reference time series from numerical weather prediction models, global assimilation databases or observations close to the area considered, the on-site measured wind speeds and wind directions are corrected in order to represent the actual long-term wind conditions. This long-term correction (LTC) is in the typical case performed by making use of the linear regression within the Measure-Correlate-Predict (MCP) method. This method and two other methods, Sector-Bin (SB) and Syntheti

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10

Iwata, Curtis. "A representation method for large and complex engineering design datasets with sequential outputs." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50266.

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This research addresses the problem of creating surrogate models of high-level operations and sustainment (O&S) simulations with time sequential (TS) outputs. O&S is a continuous process of using and maintaining assets such as a fleet of aircraft, and the infrastructure to support this process is the O&S system. To track the performance of the O&S system, metrics such as operational availability are recorded and reported as a time history. Modeling and simulation (M&S) is often used as a preliminary tool to study the impact of implementing changes to O&S systems such as investing in new techno

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11

Osunmadewa, Babatunde Adeniyi, Worku Zewdie Gebrehiwot, Elmar Csaplovics, and Olabinjo Clement Adeofun. "Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-235570.

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Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover ty

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12

Negus, Andra Stefania. "Adaptive Anomaly Detection for Large IoT Datasets with Machine Learning and Transfer Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426257.

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As more IoT devices enter the market it becomes increasingly important to develop reliable and adaptive ways of dealing with the data they generate. These must address data quality and reliability. Such solutions could benefit both the device producers and their customers who, as a result, could receive faster and better customer support services. Thus, this project's goal is twofold. First, it is to identify faulty data points generated by such devices. Second, it is to evaluate whether the knowledge gained from available/known sensors and appliances is transferable to other sensors on simila

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13

Dornala, Maninder. "Visualization of Clustering Solutions for Large Multi-dimensional Sequential Datasets." Youngstown State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1525869411092807.

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14

Merelles, Leonardo Rodrigues de Oliveira. "INFLUÊNCIA DAS MUDANÇAS CLIMÁTICAS NA PRODUTIVIDADE DE GRÃOS." Pontifícia Universidade Católica de Goiás, 2018. http://tede2.pucgoias.edu.br:8080/handle/tede/3978.

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Submitted by admin tede (tede@pucgoias.edu.br) on 2018-05-28T19:09:38Z No. of bitstreams: 1 LEONARDO RODRIGUES DE OLIVEIRA MERELLES.pdf: 1239497 bytes, checksum: cf674b2af1fe982136ca8ff2e1740f37 (MD5)<br>Made available in DSpace on 2018-05-28T19:09:38Z (GMT). No. of bitstreams: 1 LEONARDO RODRIGUES DE OLIVEIRA MERELLES.pdf: 1239497 bytes, checksum: cf674b2af1fe982136ca8ff2e1740f37 (MD5) Previous issue date: 2018-03-27<br>Understanding how climate change influences crop yield contributes to the forecasting of its consequences and assists in the management of agribusiness and food security. I

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15

Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chun

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16

Sara, Aghakhani. "Neuro-fuzzy architectures based on complex fuzzy logic." Master's thesis, 2010. http://hdl.handle.net/10048/891.

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Complex fuzzy logic is a new type of multi-valued logic, in which truth values are drawn from the unit disc of the complex plane; it is thus a generalization of the familiar infinite-valued fuzzy logic. At the present time, all published research on complex fuzzy logic is theoretical in nature, with no practical applications demonstrated. The utility of complex fuzzy logic is thus still very debatable. In this thesis, the performance of ANCFIS is evaluated. ANCFIS is the first machine learning architecture to fully implement the ideas of complex fuzzy logic, and was designed to solve the impor

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17

Liu, Chia Hao, and 劉家豪. "Implementation of a Shape Query Language for Time Series Datasets." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/49740113570519395422.

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碩士<br>國立政治大學<br>資訊科學學系<br>98<br>There are more and more time series data in the fields of medical engineering, commerce statistics, finance, etc. For example, in financial analysis, we can forecast the price trends by using some well known chart patterns. People want to find out some new patterns for making their purchase decisions fast and easily. However, it is technical challenging to implement a high-level pattern description language. This thesis implemented a shape query language for time-series datasets. Through the simple syntax, field users can find out there own shape patterns by usi

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