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Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data

Wei Ma, Zhen (Sean) Qian
2018 Transportation Research Part C: Emerging Technologies  
This paper presents a data-driven framework that estimates day-to-day dynamic OD using high-granular traffic counts and speed data collected over many years.  ...  There is a lack of methods that estimate high-resolution dynamic OD demand for a sequence of many consecutive days over several years (referred to as 24/7 OD in this research).  ...  The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The U.S.  ... 
doi:10.1016/j.trc.2018.09.002 fatcat:jyassu3zsnfvzkas65aspzjjwu

Gaussian process imputation of multiple financial series [article]

Taco de Wolff, Alejandro Cuevas, Felipe Tobar
2020 arXiv   pre-print
Learning these market dependencies among financial series is crucial for the imputation and prediction of financial observations.  ...  We compare our model against other MOGPs and the independent Gaussian process on real financial data.  ...  The red shaded areas mark the data imputation ranges. Fig. Fig. 1 shows a fit of the MOSM kernel.  ... 
arXiv:2002.05789v1 fatcat:plb7lhgparcyhfc3hykvziylgy

Imaging Time-Series to Improve Classification and Imputation [article]

Zhiguang Wang, Tim Oates
2015 arXiv   pre-print
Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields  ...  This enables the use of techniques from computer vision for time series classification and imputation.  ...  Table 2 : 2 MSE of imputation on time series using raw data and GASF images.  ... 
arXiv:1506.00327v1 fatcat:kluaxnd4wzhthlwprikgdljrse

Query optimization for dynamic imputation

José Cambronero, John K. Feser, Micah J. Smith, Samuel Madden
2017 Proceedings of the VLDB Endowment  
Users are forced to pick between removing tuples with missing values or creating a cleaned version of their data by applying a relatively expensive imputation strategy.  ...  Missing values are common in data analysis and present a usability challenge.  ...  ACKNOWLEDGMENTS The authors would like to acknowledge Akande, Li, and Reiter for giving us a cleaned copy of the 2012 ACS PUMS.  ... 
doi:10.14778/3137628.3137641 fatcat:3mvugg4g2ve47n5dlhuwmtfcoe

Missing Data Imputation with OLS-based Autoencoder for Intelligent Manufacturing

Yanxia Wang, Kang Li, Shaojun Gan, Che Cameron
2019 IEEE transactions on industry applications  
Hence, a novel OLS (orthogonal least square)based autoencoder is proposed to generate new samples for the imputation of missing values.  ...  manufacturing processes at various levels of granularity.  ...  Thus, the system is capable of collecting sufficient data at different granularity to present a hologram picture of energy usage in the factory.  ... 
doi:10.1109/tia.2019.2940585 fatcat:4mttmeppm5cfjeawzsgejh55cu

Simultaneous Missing Value Imputation and Structure Learning with Groups [article]

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2022 arXiv   pre-print
In this work, we propose VISL, a novel scalable structure learning approach that can simultaneously infer structures between groups of variables under missing data and perform missing value imputations  ...  Particularly, we propose a generative model with a structured latent space and a graph neural network-based architecture, scaling to a large number of variables.  ...  VICAUSE provides two outputs in one framework.  ... 
arXiv:2110.08223v2 fatcat:hfauxjr5uncnhjhfqk3pkrhqmm

Using Sparse Representations For Missing Data Imputation In Noise Robust Speech Recognition

Bert Cranen, Jort Gemmeke
2008 Zenodo  
Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 2008  ...  The project partners are the universities of Leuven, Nijmegen and the company Nuance.  ...  ACKNOWLEDGMENTS The research of Jort Gemmeke was carried out in the MI-DAS project, granted under the Dutch-Flemish STEVIN program.  ... 
doi:10.5281/zenodo.40917 fatcat:skpjofvgenefxctoqpjpyu5cme

Categorical Missing Data Imputation Using Fuzzy Neural Networks With Numerical And Categorical Inputs

Pilar Rey-Del-Castillo, Jesús Cardeñosa
2009 Zenodo  
A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables.  ...  The procedure is tested and compared with others using opinion poll data.  ...  This would appear to be quite a good granularity level for obtaining reliable proportions for nationwide voting intention, whereas a larger granularity would make the problem tougher.  ... 
doi:10.5281/zenodo.1333517 fatcat:2i4i2whqfzgsbesnitsv4qc5sq

Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension [article]

Ye Xue, Diego Klabjan, Yuan Luo
2019 arXiv   pre-print
The problem of missing values in multivariable time series is a key challenge in many applications such as clinical data mining.  ...  In this work, we propose a multiple imputation model that capture both cross-sectional information and temporal correlations.  ...  Researchers are increasingly motivated to build more accurate computational models from multiple types of clinical data.  ... 
arXiv:1908.04209v2 fatcat:tyw4e5naczfudkzytquj4prfpa

Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series [article]

Hong-Lan Botterman and Julien Roussel and Thomas Morzadec and Ali Jabbari and Nicolas Brunel
2022 arXiv   pre-print
We develop an online version of the batch temporal algorithm in order to process larger datasets or streaming data.  ...  We propose a robust principal component analysis (RPCA) framework to recover low-rank and sparse matrices from temporal observations.  ...  Finally, RPCA frameworks are adapted for data with entries missing completely at random. One perspective could be to learn the distributions of these missing values to better impute them.  ... 
arXiv:2208.01998v1 fatcat:piaxtf2wdfcdtert7yahixxn5a

Accurate imputation of histone modifications using transcription [article]

Zhong Wang, Alexandra G Chivu, Lauren A Choate, Edward J Rice, Donald C Miller, Tinyi Chu, Shao-Pei Chou, Nicole B Kingsley, Jessica L Peterson, Carrie J Finno, Rebecca R Bellone, Douglas F Antczak (+1 others)
2020 bioRxiv   pre-print
initiates pervasively as a consequence of open chromatin.  ...  a second pattern in stem cells was associated with transcription start sites of weakly transcribed genes.  ...  Acknowledgements We thank XSEDE allocation number TG-MCB160061 as well as an NVIDIA GPU Grant for providing computational resources required in this study.  ... 
doi:10.1101/2020.04.08.032730 fatcat:tp3wwhbj5vcdzmukwlwv6vzt3u

Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation

Ahmed Ben Said, Abdelkarim Erradi
2021 IEEE transactions on intelligent transportation systems (Print)  
Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix.  ...  Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance.  ...  ACKNLOWDGEMENT This research was made possible by NPRP 9-224-1-049 grant from the Qatar National Research Fund (a member of The Qatar Foundation).  ... 
doi:10.1109/tits.2021.3062999 fatcat:kl7r4ynhercfhgonu5ao6x7xt4

Chunk-wise regularised PCA-based imputation of missing data

A. Iodice D'Enza, A. Markos, F. Palumbo
2021 Statistical Methods & Applications  
A "chunk" is a subset of the whole set of available observations. In particular, one implementation is suitable for distributed computation as it imputes each chunk independently.  ...  This paper presents two chunk-wise implementations of RPCA suitable for the imputation of "tall" data sets, that is, data sets with many observations.  ...  We decided to use the imputation error instead of the PCA parameter recovery (as in Loisel and Takane 2019) to have a more granular measure of performance.  ... 
doi:10.1007/s10260-021-00575-5 fatcat:l5wdd7wt5vfq7fubawu2edz2z4

Generic Data Imputation and Feature Extraction for Signals from Multifunctional Printers

Jakub Valcik, Wojciech Indyk
2019 International Conference on Extending Database Technology  
Commonly, a decision support system requires a specific format and characteristic for the input data.  ...  The proposed approach has been examined on a real-world dataset of signals of printers from Konica Minolta Inc.  ...  [21] focus on data imputation for the specific problem of a time-series spatial data.  ... 
dblp:conf/edbt/ValcikI19 fatcat:yrkk6zjr2neodaq7ptsp2nryki

A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks

Difeng Zhu, Guojiang Shen, Jingjing Chen, Wenfeng Zhou, Xiangjie Kong, Yan Huang
2022 Wireless Communications and Mobile Computing  
In this paper, by leveraging motif-based graph aggregation, we propose a spatiotemporal imputation approach to address the issue of traffic data missing.  ...  Achieving accurate imputation is critical to the operation of transportation networks.  ...  Research Funds for the Provincial Universities of Zhejiang (RF-B2020001).  ... 
doi:10.1155/2022/1702170 fatcat:l3d72kmgjfeqzksbgmiqx5mitm
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