Relevant bibliographies by topics / Data series gap-filling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Data series gap-filling'

Author: Grafiati
Published: 25 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Data series gap-filling"

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Yu, Wentao, Jing Li, Qinhuo Liu, et al. "Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data." Remote Sensing 13, no. 3 (2021): 484. http://dx.doi.org/10.3390/rs13030484.

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High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the
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Keysar, Ariela. "Filling a data gap: the American Religious Identification Survey (ARIS) series." Religion 44, no. 3 (2014): 383–95. http://dx.doi.org/10.1080/0048721x.2014.903648.

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Lompar, Miloš, Branislava Lalić, Ljiljana Dekić, and Mina Petrić. "Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data." Atmosphere 10, no. 1 (2019): 13. http://dx.doi.org/10.3390/atmos10010013.

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Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. To support user communities, a technique for gap filling is developed based on the debiasing of ERA5 reanalysis data, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate. The debiasing procedure includes in situ measured temperature. The methodology is tested for dif
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Tang, Hongjie. "Missing Data Filling of Model Based on Neural Network." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 04 (2024): 649–71. http://dx.doi.org/10.1142/s0218488524400129.

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Today’s society has entered the pace of information development era. All kinds of information are digitized, while the loss of information data also directly affects the normal operation of the application system and has become the biggest obstacle to the development of information technology. The existing missing filling methods do not take into account the time-series information of the data set. Based on the neural network method for filling missing data in time series, an end-to-end missing data filling method for time series based on the residual of regression equation is proposed. Under
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SILVA, LÍVIA MARIA CAVALCANTE, FABIANO SIMPLICIO BEZERRA, MARIA CATIANA DE VASCONCELOS, MADSON RAFAEL BARBALHO DA SILVA, ANA CLÁUDIA DAVINO DOS SANTOS, and CERES DUARTE GUEDESCABRAL DE ALMEIDA. "GAP FILLING PROCEDURES OF CLIMATOLOGICAL SERIES IN THE STATE OF PERNAMBUCO." IRRIGA 1, no. 4 (2021): 754–64. http://dx.doi.org/10.15809/irriga.2021v1n4p754-764.

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This study aimed to compare the applicability of three methods of filling gaps in rainfall and temperature data from thirteen automatic weather stations (AWS) in the state of Pernambuco, from January to December 2019. The methods used were arithmetic mean, regional weighting, and simple linear regression. The data estimated by filling techniques have been subjected to comparison using R² and descriptive statistical analysis. The estimated data of air temperature presented R2 equal or very close to 1 for the three methods. On the other hand, the estimated data of rainfall showed values similar
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Boudhina, Nissaf, Rim Zitouna-Chebbi, Insaf Mekki, et al. "Evaluating four gap-filling methods for eddy covariance measurements of evapotranspiration over hilly crop fields." Geoscientific Instrumentation, Methods and Data Systems 7, no. 2 (2018): 151–67. http://dx.doi.org/10.5194/gi-7-151-2018.

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Abstract. Estimating evapotranspiration in hilly watersheds is paramount for managing water resources, especially in semiarid/subhumid regions. The eddy covariance (EC) technique allows continuous measurements of latent heat flux (LE). However, time series of EC measurements often experience large portions of missing data because of instrumental malfunctions or quality filtering. Existing gap-filling methods are questionable over hilly crop fields because of changes in airflow inclination and subsequent aerodynamic properties. We evaluated the performances of different gap-filling methods befo
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Sharma, Nalin, Prasun Kumar Gupta, and Prabhakar Alok Verma. "Temporal Gap Filling of Nighttime Light Composites." Journal of Geomatics 19, no. 1 (2025): 29–38. https://doi.org/10.58825/jog.2025.19.1.152.

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The temporal nighttime light (NTL) data generated by DMSP-OLS sensors was discovered to have large gaps (missing values) over time. The research aims to provide a scientifically valid gap-filling mechanism for having consistent DMPS-OLS time series data (1992-2013) and predicting the historic NTL (1991-1985) for long-term studies. A deep learning neural network, Long Short Term Memory (LSTM) has been proposed in the study for temporal gap filling and historic NTL prediction. The developed LSTM model is being tested in a time distributed wrapper way having window size (3-7) for the temporal gap
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Kang, Minseok, Kazuhito Ichii, Joon Kim, et al. "New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach." Atmosphere 10, no. 10 (2019): 568. http://dx.doi.org/10.3390/atmos10100568.

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In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/sp
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Beguería, Santiago, Miquel Tomas-Burguera, Roberto Serrano-Notivoli, Dhais Peña-Angulo, Sergio M. Vicente-Serrano, and José-Carlos González-Hidalgo. "Gap Filling of Monthly Temperature Data and Its Effect on Climatic Variability and Trends." Journal of Climate 32, no. 22 (2019): 7797–821. http://dx.doi.org/10.1175/jcli-d-19-0244.1.

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Abstract Observational datasets of climatic variables are frequently composed of fragmentary time series covering different time spans and plagued with data gaps. Most statistical methods and environmental models, however, require serially complete data, so gap filling is a routine procedure. However, very often this preliminary stage is undertaken with no consideration of the potentially adverse effects that it can have on further analyses. In addition to numerical effects and trade-offs that are inherent to any imputation method, observational climatic datasets often exhibit temporal changes
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Pascual-Granado, J., R. Garrido, J. Gutirrez-Soto, and S. Martín-Ruiz. "Towards a More General Method for Filling Gaps in Time Series." Proceedings of the International Astronomical Union 7, S285 (2011): 392–93. http://dx.doi.org/10.1017/s1743921312001172.

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AbstractThe need for a proper interpolation method for data coming from space missions like CoRoT is emphasized. A new gap-filling method is introduced which is based on auto-regressive moving average interpolation (ARMA) models. The method is tested on light curves from stars observed by the CoRoT satellite, filling the gaps caused by the South Atlantic Anomaly.
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Dissertations / Theses on the topic "Data series gap-filling"

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Tardivo, Gianmarco. "Methods for gap filling in long term meteorological series and correlation analysis of meteorological networks." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3422634.

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Climate data are very useful in many fields of the scientific research. Nowadays, in many cases these data are available through giant data-base that are often yielded by automatic meteorological networks. In order to make possible research analysis and the running of computational models, these data base need to be validated, homogenized, and to be without missing values. Validation and homogenization are common operations, nowadays: the organizations that manage these data-base provide these services. The main problem remain the reconstruction of the missing data. This dissertation d
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Rodrigues, Mutti Pedro. "Caractérisation de la sécheresse dans le nord-est du Brésil : une analyse multi-échelle des bassins versants et suivi par télédétection." Thesis, Rennes 2, 2020. http://www.theses.fr/2020REN20036.

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La sécheresse est un phénomène récurrent dans la région du Nord-Est du Brésil (NEB), en particulier dans les régions semi-arides de l’intérieur. Bien que plusieurs études de la sécheresse aient été menées au NEB, certains aspects méthodologiques inhérents à la qualité des données, aux spécificités des techniques utilisées et à l'échelle spatiale ont été encore peu discutés. Dans ce contexte, l'objectif de cette thèse est de caractériser les différents aspects de la sécheresse au NEB en considérant les caractéristiques des données météorologiques, les différentes échelles spatiales et les alter
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Book chapters on the topic "Data series gap-filling"

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Mordick, Briana E. "Filling the Data Gap: What We Know (and Don’t Know) about Hydraulic Fracturing and Acidizing in California." In ACS Symposium Series. American Chemical Society, 2015. http://dx.doi.org/10.1021/bk-2015-1216.ch010.

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Sarafanov, Mikhail, Nikolay O. Nikitin, and Anna V. Kalyuzhnaya. "Automated Data-Driven Approach for Gap Filling in the Time Series Using Evolutionary Learning." In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87869-6_60.

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Trillo, Claudia, Rania Aburamadan, Victoria Andrea Cotella, Chiko Ncube Makore, and Qassim Al-Betar. "A Novel Application of City Information Modelling: Filling the Gap in the Data Through Better Citizens’ Engagement. Insights from Al Baqa’, Jordan." In Sustainable Development Goals Series. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36640-6_28.

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Conference papers on the topic "Data series gap-filling"

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Fischer, Raphael, Nico Piatkowski, Charlotte Pelletier, Geoffrey I. Webb, Francois Petitjean, and Katharina Morik. "No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00069.

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Tunkiel, Andrzej T., Dan Sui, and Tomasz Wiktorski. "Automated Iterative Gap Filling Method for Drilling Logs and Other Data Series." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-61927.

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Abstract Data scientists are facing multiple issues when working with real-life data. Logs are rarely devoid of incorrect values and one of the common categories of data problems is missing values. Gaps in logs are of various shapes, sizes, and quantities, with a plethora of techniques to infill, or restore missing values. No single algorithm will perform best for all scenarios, hence in pursuit of best results exploration of various options is necessary. Furthermore, gap filling in single step may be impossible for certain methods, where gaps exist for multiple attributes. This paper explores
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Sengupta, Manajit, Soutir Bandyopadhyay, Aron Habte, and Alexis Denhard. "A Comparison of Time Series Gap-Filling Methods to Impute Solar Radiation Data." In ISES Solar World Congress 2021. International Solar Energy Society, 2021. http://dx.doi.org/10.18086/swc.2021.38.03.

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Camilleri, Anne-Marie, Joel Azzopardi, and Adam Gauci. "A Novel Approach towards Gap Filling of High-Frequency Radar Time-series Data." In 14th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011540400003335.

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Baltazar, Juan-Carlos, and David E. Claridge. "Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data." In ASME Solar 2002: International Solar Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/sed2002-1031.

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A study of cubic splines and Fourier series as interpolation techniques for filling in missing data in energy and meteorological time series is presented. The followed procedure created artificially missing points (pseudo-gaps) in measured data sets and was based on the local behavior of the data set around those pseudo-gaps. Five variants of the cubic spline technique and 12 variants of Fourier series were tested and compared with linear interpolation, for filling in gaps of 1 to 6 hours of data in 20 samples of energy use and weather data. Each of the samples is at least one year in length.
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Golestani, Maziar, and Mostafa Zeinoddini. "Gap-Filling and Predicting Wave Parameters Using Support Vector Regression Method." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49814.

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Knowledge of relevant oceanographic parameters is of utmost importance in the rational design of coastal structures and ports. Therefore, an accurate prediction of wave parameters is especially important for safety and economic reasons. Recently, statistical learning methods, such as Support Vector Regression (SVR) have been successfully employed by researchers in problems such as lake water level predictions, and significant wave height prediction. The current study reports potential application of a SVR approach to predict the wave spectra and significant wave height. Also the capability of
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Chen, Jiaxin, Ian G. C. Ashton, and Ajit C. Pillai. "Wave Record Gap-Filling Using a Low-Rank Tensor Completion Model." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-79897.

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Abstract The offshore wind farm industry has identified further refinement of marine operations as necessary to realize the lower strike prices seen in recent subsidy auctions. This requires extending working times by taking advantage of weather windows, even when operating in more remote sites. Key to this will be increasing the accuracy of forecasts and live metocean data from site for effective and safe operation scheduling. In recent years, statistical models or deep learning-based models, by learning spatial and temporal patterns in observations have shown their potential to support or ev
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GLAUBITZ, Claudia. "Laser triangulation for quality monitoring in automated series forging processes: A method for evaluating the component quality feature 'flash'." In Material Forming. Materials Research Forum LLC, 2025. https://doi.org/10.21741/9781644903599-98.

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Abstract. Flash formation is a characteristic feature of impression die forging, resulting from the expulsion of excess material through the gap between the upper and lower dies. This expulsion is a consequence of the backpressure generated by the material flow, which ensures complete filling of the die cavity. However, this increases material consumption and requires additional post-processing to remove the flash. Flash formation is influenced by process parameters such as die closure, workpiece temperature, forming speed, forming force and lubrication. Improper control of these parameters ca
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Reports on the topic "Data series gap-filling"

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Volpe Martincus, Christian, and Jerónimo Carballo. Export Promotion: Heterogeneous Programs and Heterogeneous Effects. Inter-American Development Bank, 2010. http://dx.doi.org/10.18235/0011216.

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Export promotion agencies provide exporters with a broad range of services, going from counseling to sponsoring their participation in international trade missions and fairs. These services may have heterogeneous effects and thus contribute differently to achieve the goals of these organizations. Empirical evidence on their relative effectiveness is rather limited. This paper aims at filling this gap in the literature. We compare the impact of different public trade promotion programs on the extensive and intensive margin of firms¿ exports, both to each other and with respect to no participati
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