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Saul G. Ramirez, Gustavious Paul Williams, Norman L. Jones, Daniel P. Ames and Jani Radebaugh
Obtaining and managing groundwater data is difficult as it is common for time series datasets representing groundwater levels at wells to have large gaps of missing data. To address this issue, many methods have been developed to infill or impute the mis...
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Ren Nishimura, Norman L. Jones, Gustavious P. Williams, Daniel P. Ames, Bako Mamane and Jamila Begou
Accurate characterization of groundwater resources is required for sustainable management. Due to the cost of installing monitoring wells and challenges in collecting and managing in situ data, groundwater data are sparse?especially in developing countri...
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Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang and Rui Wu
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis. Therefore, data imputation needs to be performed first. ...
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Benjamin Nelsen, D. Alexandra Williams, Gustavious P. Williams and Candace Berrett
Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have...
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Mahrokh Moknatian and Michael Piasecki
This paper presents the development of an evenly spaced volume time series for Lakes Azuei and Enriquillo both located on the Caribbean island of Hispaniola. The time series is derived from an unevenly spaced Landsat imagery data set which is then expose...
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Stéphane Crépey, Noureddine Lehdili, Nisrine Madhar and Maud Thomas
A major concern when dealing with financial time series involving a wide variety of market risk factors is the presence of anomalies. These induce a miscalibration of the models used to quantify and manage risk, resulting in potential erroneous risk meas...
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Edgar Acuna, Roxana Aparicio and Velcy Palomino
In this paper we investigate the effect of two preprocessing techniques, data imputation and smoothing, in the prediction of blood glucose level in type 1 diabetes patients, using a novel deep learning model called Transformer. We train three models: XGB...
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Fahima Noor, Sanaulla Haq, Mohammed Rakib, Tarik Ahmed, Zeeshan Jamal, Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan and Rashedur M. Rahman
Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water ...
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Xinxi Lu, Lijuan Yuan, Ruifeng Li, Zhihuan Xing, Ning Yao and Yichun Yu
In recent years, the development of computer technology has promoted the informatization and intelligentization of hospital management systems and thus produced a large amount of medical data. These medical data are valuable resources for research. We ca...
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Jorge Luis Morales,Francisco Antonio Horta -Rangel,Ignacio Segovia-Domínguez,Agustín Robles Morua,Jesús Horacio Hernández
Pág. 237 - 259
In the present work, two new generalized weighted methods of imputation of missing data are developed and tested using a daily rainfall series. The proposed methodology allows to fully rebuild the time series while preserving its statistical properties. ...
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