Time series generation model based on multi-discriminator generative adversarial network by Yanhui LU, Han LIU, Hang LI, Guangxu ZHU

“…Aiming at the problems of expensive collection cost and missing data due to the privacy and continuity of time series data set, a multi-discriminator generative adversarial network model based on recurrent neural network was proposed, which could synthesize time series dataset that were approximately distributed with real data of a small scale dataset.Multi-discriminator included four discriminators in time domain, frequency domain, time-frequency domain and autocorrelation.Different discriminators could effectively recognize the features of the time series in different domains.In the experiment, the convergence of loss function, principal component analysis and error analysis were performed to evaluate the performance of the model from qualitative and quantitative perspectives.The experimental results show that the proposed model has better performance than other reference models.…”
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A Data-Driven Approach to Estimate Incident-Induced Delays Using Incomplete Probe Vehicle Data: Application to Safety Service Patrol Program Evaluation by Minsoo Oh, Jing Dong-O’Brien

“…This paper presents a data-driven approach to estimate incident-induced delays (IIDs) using probe vehicle data while accounting for missing data. The proposed approach is applied to evaluate the effectiveness of a safety service patrol (SSP) program. …”
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A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery by Xintong Zou, Hui Jin

“…To address these issues, an improved matrix decomposition model was designed to interpolate the missing data by taking into account the spatiotemporal correlation between warehouses. …”
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Calculations of Cross-Sections for Positron Scattering on Benzene by Małgorzata Franz, Anna Pastuszko, Jan Franz

“…The aim of this work is to provide missing data from partial cross-sections for specific processes. …”
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An Algorithm Using DBSCAN to Solve the Velocity Dealiasing Problem by Wei Zhao, Qinglan Li, Kuifeng Jin

“…The results of the case study also show that the 4DD algorithm filters out many observation gates close to the missing data or radar center, whereas the proposed algorithm tends to retain and correct these gates.…”
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Joint recommendation algorithm based on tensor completion and user preference by Zhi XIONG, Kai XU, Lingru CAI, Weihong CAI

“…Aiming at the problem that existing recommendation algorithms have little regard for user preference,and the recommendation result is not satisfactory,a joint recommendation algorithm based on tensor completion and user preference was proposed.First,a user-item-category 3-dimensional tensor was built based on user-item scoring matrix and item-category matrix.Then,the Frank-Wolfe algorithm was used for iterative calculation to fill in the missing data of the tensor.At the same time,a user category preference matrix and a scoring preference matrix were built based on the 3-dimensional tensor.Finally,a joint recommendation algorithm was designed based on the completed tensor and the two preference matrices,and the differential evolution algorithm was used for parameter tuning.The experimental results show that compared with some typical and newly proposed recommendation algorithms,the proposed algorithm is superior to the compare algorithms,the precision is improved by 1.96% ~ 3.44% on average,and the recall rate is improved by 1.35%~2.40% on average.…”
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Hierarchical Bayesian Spatio-Temporal Modeling for PM10 Prediction by Esam Mahdi, Sana Alshamari, Maryam Khashabi, Alya Alkorbi

“…The disaggregating technique with a Markov chain Monte Carlo method with Gibbs sampler are used to handle the missing data. Based on the obtained results, we conclude that the Gaussian predictive processes with autoregressive terms of the latent underlying space-time process model is the best, compared with the Bayesian Gaussian processes and non-Bayesian generalized additive models.…”
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NutriBase – management system for the integration and interoperability of food- and nutrition-related data and knowledge by Eva Valenčič, Eva Valenčič, Eva Valenčič, Eva Valenčič, Emma Beckett, Emma Beckett, Emma Beckett, Tamara Bucher, Tamara Bucher, Clare E. Collins, Clare E. Collins, Barbara Koroušić Seljak, Barbara Koroušić Seljak

“…The system is designed to allow easy integration of data from different sources, which enables data borrowing and reduction of missing data. In this paper, the feasibility of NutriBase is demonstrated on Slovenian food-related data and knowledge, which is further linked with international resources. …”
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Predictive Modeling of Chronic Kidney Disease Progression with Ensemble Learning Techniques by priha bhatti

“…Utilizing an extensive dataset, the study employs ten carefully designed stages, covering data analysis, missing data management, normalization, and training of machine learning models. …”
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Performance evaluation of different estimation methods for missing rainfall data

“…In order to study the effectiveness of various methods to estimate missing data, by seven classic statistical methods and M5 model tree as one of efficient data mining methods, hypothetical missing values were estimated using precipitation data from neighbor station. …”
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Accelerated dynamic magnetic resonance imaging from Spatial-Subspace Reconstructions (SPARS). by Alexander J Mertens, Hai-Ling Margaret Cheng

“…Even state-of-the-art image reconstruction techniques that infer missing data in a sparse acquisition space cannot recover the loss of spatial detail, especially at high temporal acceleration rates. …”
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A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

“…Additionally, traditional methods often struggle with noisy or missing data. To address these issues, we propose novel classification methods based on feature partitioning and outranking measures. …”
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Flexible and modular latent transition analysis-A tutorial using R. by Lisbeth Lund, Christian Ritz

“…The proposed alternative approach offers more options in terms of choice of effect measures, model assumptions such as hierarchical structures and covariate adjustment, and differential handling of missing data. R code snippets are provided in the tutorial. …”
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Distribution Inference for Physical and Orbital Properties of Jupiter’s Moons by F. B. Gao, X. H. Zhu, X. Liu, R. F. Wang

“…Based on the inferred results, one can predict some physical or orbital features of moons with missing data or even new possible moons within a reasonable range. …”
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Analytical validation of the IBD segment-based tool KinSNP® for human identification applications by Bruce Budowle, Jianye Ge, Lee Baker, Kristen Mittelman, David Mittelman

“…The calculated values from KinSNP aligned closely with IBIS and Ped-sim benchmarks, and accuracy was maintained with up to 75% simulated missing data. However, even slight increases in simulated sequence error rates negatively impacted performance. …”
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Detection of dynamic communities in temporal networks with sparse data by Nataša Djurdjevac Conrad, Elisa Tonello, Johannes Zonker, Heike Siebert

“…However, when working with real-world systems, available data is often limited and sparse, due to missing data on systems entities, their evolution and interactions, as well as uncertainty regarding temporal resolution. …”
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A Long Short-Term Memory–Wasserstein Generative Adversarial Network-Based Data Imputation Method for Photovoltaic Power Output Prediction by Zhu Liu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie, Dongguo Zhou

“…To address the challenges of the issue of inaccurate prediction results due to missing data in PV power records, a photovoltaic power data imputation method based on a Wasserstein Generative Adversarial Network (WGAN) and Long Short-Term Memory (LSTM) network is proposed. …”
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Later-Life Mortality and Longevity in Late-18th and 19th-Century Cohorts. Where Are We Now, and Where Are We Heading? by Rick Mourits

“…However, in order to find out the determinants of later-life mortality, external validity of results, blind spots due to missing data, and familial clustering need to be studied more thoroughly.…”
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Stroke Prediction Based on Machine Learning by Zhang Yuhan

“…Data preprocessing encompasses managing missing data, processing categorical variables, and tackling issues related to class imbalance. …”
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A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica by Xin Zou, Wen Long Yue

“…By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. …”
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