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Title: GitHub - geothinking/transdim: Machine learning for transportation data imputation and prediction.

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Description: Machine learning for transportation data imputation and prediction. - geothinking/transdim

Open Graph Description: Machine learning for transportation data imputation and prediction. - geothinking/transdim

X Description: Machine learning for transportation data imputation and prediction. - geothinking/transdim

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https://github.com/xinychen/transdim/archive/master.zip
https://github.com/xinychen/transdim
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https://patch-diff.githubusercontent.com/geothinking/transdim#about-the-project
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https://github.com/xinychen/transdim/blob/master/images/framework.png
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https://patch-diff.githubusercontent.com/geothinking/transdim#implementation
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Guangzhou urban traffic speed data sethttps://doi.org/10.5281/zenodo.1205228
Birmingham parking data sethttps://archive.ics.uci.edu/ml/datasets/Parking+Birmingham
Hangzhou metro passenger flow data sethttps://doi.org/10.5281/zenodo.3145403
NYC taxi data sethttps://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
Seattle freeway traffic speed data sethttps://github.com/zhiyongc/Seattle-Loop-Data
https://patch-diff.githubusercontent.com/geothinking/transdim#model-implementation
BTMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BTMF.ipynb
BayesTRMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BayesTRMF.ipynb
TRMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-TRMF.ipynb
BPMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BPMF.ipynb
BGCPhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BGCP.ipynb
TF-ALShttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-TF-ALS.ipynb
BTTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BTTF.ipynb
BayesTRTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BayesTRTF.ipynb
BPTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Imputation-BPTF.ipynb
BTMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-ST-Online-BTMF.ipynb
BayesTRMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-ST-Online-BayesTRMF.ipynb
TRMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-ST-Online-TRMF.ipynb
BTTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-ST-Online-BTTF.ipynb
BayesTRTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-ST-Online-BayesTRTF.ipynb
TRTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-ST-Online-TRTF.ipynb
BTMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-Multi-BTMF.ipynb
BayesTRMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-Multi-BayesTRMF.ipynb
TRMFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-Multi-TRMF.ipynb
BTTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-Multi-BTTF.ipynb
BayesTRTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-Multi-BayesTRTF.ipynb
TRTFhttps://nbviewer.jupyter.org/github/xinychen/transdim/blob/master/experiments/Prediction-Multi-TRTF.ipynb
https://patch-diff.githubusercontent.com/geothinking/transdim#imputationprediction-performance
https://github.com/xinychen/transdim/blob/master/images/estimated_series1.png
https://github.com/xinychen/transdim/blob/master/images/estimated_series2.png
https://github.com/xinychen/transdim/blob/master/images/prediction_hangzhou.png
https://github.com/xinychen/transdim/blob/master/images/prediction_nyc_heatmap.png
https://github.com/xinychen/transdim/blob/master/images/prediction_nyc.png
https://patch-diff.githubusercontent.com/geothinking/transdim#references
https://patch-diff.githubusercontent.com/geothinking/transdim#spatiotemporal-forecasting
Deep Factors for Forecastinghttps://arxiv.org/pdf/1905.12417.pdf
Deep Factors with Gaussian Processes for Forecastinghttps://arxiv.org/pdf/1812.00098.pdf
Deep State Space Models for Time Series Forecastinghttp://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting.pdf?nsukey=CoqM71tHuwiqTTDwkPRsu40QKdc%2BlY%2FWWNEiuEJaWd6sw7dUXlAT3mU122dvYrMHOYfANParRFLBDLgraANFjHdggEDRxbT9Kk2Mam6nZfVkQT4E2cRCMULUTPTFmjCeuBKHjDSSEs6L4Ci7JuyRqB2ojJnBKDD%2FkcAgdhwhJQa%2BLZ5owi4oVwnQrgWX5pTr0vTORENMdC59F4mqtOQENA%3D%3D
Online Forecasting Matrix Factorizationhttps://ieeexplore.ieee.org/document/8590686/
Python codehttps://github.com/chloemnge/online_learning
HyperST-Net: hypernetworks for spatio-temporal forecastinghttps://arxiv.org/pdf/1809.10889.pdf
Local Gaussian processes for efficient fine-grained traffic speed predictionhttps://arxiv.org/pdf/1708.08079.pdf
A brief overview of machine learning methods for short-term traffic forecasting and future directionshttps://doi.org/10.1145/3231541.3231544
Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecastinghttps://arxiv.org/pdf/1709.04875.pdf
appear in IJCAI 2018https://www.ijcai.org/proceedings/2018/0505.pdf
Temporally-reweighted Chinese Restaurant Process mixtures for clustering, imputing, and forecasting multivariate time serieshttp://proceedings.mlr.press/v84/saad18a/saad18a.pdf
Recurrent neural networks for multivariate time series with missing valueshttps://doi.org/10.1038/s41598-018-24271-9
Hierarchical deep generative models for multi-rate multivariate time serieshttp://proceedings.mlr.press/v80/che18a/che18a.pdf
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series datahttps://arxiv.org/abs/1811.08055
Efficient metropolitan traffic prediction based on graph recurrent neural networkhttps://arxiv.org/pdf/1811.00740.pdf
High-order temporal correlation model learning for time-series predictionhttps://doi.org/10.1109/TCYB.2018.2832085
Online time series prediction with missing datahttp://proceedings.mlr.press/v37/anava15.pdf
POI2Vec: Geographical latent representation for predicting future visitorshttps://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14902/13749
Fast mining and forecasting of complex time-stamped eventshttp://www.cs.kumamoto-u.ac.jp/~yasuko/PUBLICATIONS/kdd12-trimine.pdf
FUNNEL: automatic mining of spatially coevolving epidemicshttp://www.cs.cmu.edu/~christos/PUBLICATIONS/14-kdd-funnel.pdf
Autoregressive tensor factorization for spatio-temporal predictionshttps://doi.org/10.1109/ICDM.2017.146
Temporal pattern attention for multivariate time series forecastinghttps://arxiv.org/pdf/1809.04206v2.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#principal-component-analysis
A Bayesian missing value estimation method for gene expression profile datahttps://doi.org/10.1093/bioinformatics/btg287
Matlab codehttp://ishiilab.jp/member/oba/tools/BPCAFill.html
PPCA-based missing data imputation for traffic flow volume: a systematical approachhttps://doi.org/10.1109/TITS.2009.2026312
Efficient missing data imputing for traffic flow by considering temporal and spatial dependencehttps://doi.org/10.1016/j.trc.2013.05.008
https://patch-diff.githubusercontent.com/geothinking/transdim#guassian-process
Markov chain Monte Carlo algorithms for Gaussian processeshttp://www2.aueb.gr/users/mtitsias/papers/ILDMChapter09.pdf
Multi-output Gaussian processes for crowdsourced traffic data imputationhttps://doi.org/10.1109/TITS.2018.2817879
Matlab codehttp://fprodrigues.com/publications/multi-output-gaussian-processes-for-crowdsourced-traffic-data-imputation/
Probabilistic matrix factorization for automated machine learninghttps://arxiv.org/pdf/1705.05355.pdf
Python codehttps://github.com/elibol/amle
Kernelized probabilistic matrix factorization: exploiting graphs and side informationhttp://www.cs.cmu.edu/~tinghuiz/papers/sdm12_kpmf.pdf
slidehttp://people.ee.duke.edu/~lcarin/Jorge6.4.2012.pdf
Adversarial examples, uncertainty, and transfer testing robustness in Gaussian process hybrid deep networkshttps://arxiv.org/pdf/1707.02476.pdf
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processeshttps://arxiv.org/pdf/1910.03002.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#matrix-factorization
Collaborative filtering with graph information: Consistency and scalable methodshttp://www.cs.utexas.edu/~rofuyu/papers/grmf-nips.pdf
Matlab codehttp://bigdata.ices.utexas.edu/publication/collaborative-filtering-with-graph-information-consistency-and-scalable-methods/
Temporal regularized matrix factorization for high-dimensional time series predictionhttp://www.cs.utexas.edu/~rofuyu/papers/tr-mf-nips.pdf
Matlab codehttps://github.com/rofuyu/exp-trmf-nips16
Network-wide crowd flow prediction of Sydney trains via customized online non-negative matrix factorizationhttp://urban-computing.com/pdf/CIKM18-1121-Camera%20Ready.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#bayesian-matrix-and-tensor-factorization
Bayesian probabilistic matrix factorization using Markov chain Monte Carlohttps://www.cs.toronto.edu/~amnih/papers/bpmf.pdf
Matlab code (official)https://www.cs.toronto.edu/~rsalakhu/BPMF.html
Python codehttps://github.com/LoryPack/BPMF
Julia and C++ codehttps://github.com/ExaScience/bpmf
Julia codehttps://github.com/RottenFruits/BPMF.jl
Non-linear Matrix Factorization with Gaussian Processeshttp://people.ee.duke.edu/~lcarin/MatrixFactorization.pdf
Modelling relational data using Bayesian clustered tensor factorizationhttps://ece.duke.edu/~lcarin/pmfcrp.pdf
Learning evolving and emerging topics in social media: A dynamic NMF approach with temporal regularizationhttp://people.cs.uchicago.edu/~ankans/Papers/wsdm227-saha.pdf
Probabilistic matrix factorization for automated machine learninghttps://arxiv.org/pdf/1705.05355.pdf
Temporal collaborative filtering with Bayesian probabilistic tensor factorizationhttps://www.cs.cmu.edu/~jgc/publication/PublicationPDF/Temporal_Collaborative_Filtering_With_Bayesian_Probabilidtic_Tensor_Factorization.pdf
Bayesian CP factorization of incomplete tensors with automatic rank determinationhttps://doi.org/10.1109/TPAMI.2015.2392756
Bayesian sparse Tucker models for dimension reduction and tensor completionhttps://arxiv.org/pdf/1505.02343.pdf
Scalable Bayesian low-rank decomposition of incomplete multiway tensorshttp://people.ee.duke.edu/~lcarin/mpgcp.pdf
Probabilistic sequential matrix factorizationhttps://arxiv.org/pdf/1910.03906.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#matrix-completion-on-graphs
Matrix completion on graphshttps://arxiv.org/abs/1408.1717
Graph convolutional matrix completionhttps://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_32.pdf
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkshttps://papers.nips.cc/paper/6960-geometric-matrix-completion-with-recurrent-multi-graph-neural-networks.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#low-rank-tensor-completion
Tensor completion for estimating missing values in visual datahttps://doi.org/10.1109/TPAMI.2012.39
Tensor based missing traffic data completion with spatial–temporal correlationhttps://doi.org/10.1016/j.physa.2015.09.105
https://patch-diff.githubusercontent.com/geothinking/transdim#generative-adversarial-nets
Image completion with deep learning in TensorFlowhttp://bamos.github.io/2016/08/09/deep-completion/
githubhttps://github.com/bamos/dcgan-completion.tensorflow
GAIN: missing data imputation using Generative Adversarial Netshttp://proceedings.mlr.press/v80/yoon18a/yoon18a.pdf
supplementary materialshttp://medianetlab.ee.ucla.edu/papers/ICML_GAIN_Supp.pdf
Python codehttps://github.com/jsyoon0823/GAIN
NIPS 2016 tutorial: Generative Adversarial Networkshttps://arxiv.org/abs/1701.00160
Unsupervised anomaly detection with generative adversarial networks to guide marker discoveryhttps://arxiv.org/abs/1703.05921
Multivariate time series imputation with generative adversarial networkshttps://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks
Python codehttps://github.com/Luoyonghong/Multivariate-Time-Series-Imputation-with-Generative-Adversarial-Networks
https://patch-diff.githubusercontent.com/geothinking/transdim#variational-autoencoder
Factorized variational autoencoders for modeling audience reactions to movieshttp://openaccess.thecvf.com/content_cvpr_2017/papers/Deng_Factorized_Variational_Autoencoders_CVPR_2017_paper.pdf
Graph autoencoder - GitHubhttps://github.com/tkipf/gae
Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applicationshttps://arxiv.org/pdf/1802.03903.pdf
Variational Autoencoders for missing data imputation with application to a simulated milling circuithttps://doi.org/10.1016/j.ifacol.2018.09.406
Python codehttps://github.com/ProcessMonitoringStellenboschUniversity/IFAC-VAE-Imputation
VAE demohttps://github.com/oduerr/dl_tutorial/blob/master/tensorflow/vae/vae_demo-2D.ipynb
missingIWAE: Deep generative modelling and imputation of incomplete datahttp://bayesiandeeplearning.org/2018/papers/100.pdf
related slidehttps://ai.ku.dk/ai-seminar-series/ai-seminar_jes-frellsen.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#tensor-regression
Low-rank regression with tensor responseshttps://papers.nips.cc/paper/6302-low-rank-regression-with-tensor-responses.pdf
Learning from multiway data: simple and efficient tensor regressionhttp://proceedings.mlr.press/v48/yu16.pdf
Doubly decomposing nonparametric tensor regressionhttp://proceedings.mlr.press/v48/imaizumi16.pdf
Tensor regression meets Gaussian processeshttp://proceedings.mlr.press/v84/yu18a/yu18a.pdf
Matlab codehttps://github.com/yuqirose/MultilinearGP
Boosted sparse and low-rank tensor regressionhttps://papers.nips.cc/paper/7379-boosted-sparse-and-low-rank-tensor-regression.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#poisson-matrix-factorization
Poisson matrix factorizationhttp://www.hongliangjie.com/2015/08/17/poisson-matrix-factorization/
Bayesian inference for nonnegative matrix factorisation modelshttp://downloads.hindawi.com/journals/cin/2009/785152.pdf
Scalable recommendation with hierarchical poisson factorizationhttp://www.cs.columbia.edu/~blei/papers/GopalanHofmanBlei2015.pdf
C++ codehttps://github.com/premgopalan/hgaprec
Dynamic Poisson factorizationhttp://www.cs.toronto.edu/~lcharlin/papers/2015_CharlinRanganathMcInerneyBlei.pdf
C++ codehttps://github.com/blei-lab/DynamicPoissonFactorization
Recurrent Poisson factorization for temporal recommendationhttps://dl.acm.org/citation.cfm?doid=3097983.3098197
Matlab codehttps://github.com/AHosseini/RPF
Poisson-Randomized Gamma Dynamical Systemshttps://arxiv.org/pdf/1910.12991.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#graph-signal-processing
A graph signal processing approach for real-time traffic prediction in transportation networkshttps://arxiv.org/pdf/1711.06954.pdf
Graph signal processing: overview, challenges, and applicationshttps://doi.org/10.1109/JPROC.2018.2820126
slidehttps://www.seas.upenn.edu/~gsp16/ortega.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#graph-neural-network
How to do Deep Learning on Graphs with Graph Convolutional Networks (Part 1: A High-Level Introduction to Graph Convolutional Networks)https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780
Structured deep models: Deep learning on graphs and beyondhttps://tkipf.github.io/misc/SlidesCambridge.pdf
gcn: Implementation of Graph Convolutional Networks in TensorFlowhttps://github.com/tkipf/gcn
gated-graph-neural-network-samples: Sample Code for Gated Graph Neural Networkshttps://github.com/Microsoft/gated-graph-neural-network-samples
Spatiotemporal multi-graph convolution network for ride-hailing demand forecastinghttp://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_convolution.pdf
Dynamic spatio-temporal graph-based CNNs for traffic predictionhttps://arxiv.org/pdf/1812.02019.pdf
https://patch-diff.githubusercontent.com/geothinking/transdim#missing-data-imputation
MissForest—non-parametric missing value imputation for mixed-type datahttps://doi.org/10.1093/bioinformatics/btr597
missingpy - PyPIhttps://pypi.org/project/missingpy/
missingpy - GitHubhttps://github.com/epsilon-machine/missingpy
fancyimputehttps://github.com/iskandr/fancyimpute
homepagehttps://pypi.org/project/fancyimpute/
From predictive methods to missing data imputation: An optimization approachhttp://jmlr.org/papers/v18/17-073.html
BRITS: Bidirectional Recurrent Imputation for Time Serieshttps://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series.pdf
Python codehttps://github.com/caow13/BRITS
https://patch-diff.githubusercontent.com/geothinking/transdim#our-publications
preprinthttps://arxiv.org/abs/1910.06366
slidehttps://xinychen.github.io/paper/Bayesian-temporal-factorization-slide.pdf
data & Python codehttps://github.com/xinychen/transdim
preprinthttps://xinychen.github.io/paper/BATF.pdf
doihttps://doi.org/10.1016/j.trc.2019.03.003
slidehttps://doi.org/10.5281/zenodo.2632552
datahttp://doi.org/10.5281/zenodo.1205229
Matlab codehttps://github.com/sysuits/BATF
preprinthttps://www.researchgate.net/publication/329177786_A_Bayesian_tensor_decomposition_approach_for_spatiotemporal_traffic_data_imputation
doihttps://doi.org/10.1016/j.trc.2018.11.003
datahttp://doi.org/10.5281/zenodo.1205229
Matlab codehttps://github.com/lijunsun/bgcp_imputation
Python codehttps://github.com/xinychen/transdim/blob/master/experiments/Imputation-BGCP.ipynb
doihttp://doi.org/10.1016/j.trc.2017.10.023
datahttp://doi.org/10.5281/zenodo.1205229
https://patch-diff.githubusercontent.com/geothinking/transdim#collaborators
Xinyu Chenhttps://github.com/xinychen
💻https://github.com/xinychen/transdim/commits?author=xinychen
Jinming Yanghttps://github.com/Vadermit
💻https://github.com/xinychen/transdim/commits?author=Vadermit
Yixian Chenhttps://github.com/yxnchen
💻https://github.com/xinychen/transdim/commits?author=yxnchen
Lijun Sunhttps://github.com/lijunsun
💻https://github.com/xinychen/transdim/commits?author=lijunsun
Tianyang Hanhttps://github.com/HanTY
💻https://github.com/xinychen/transdim/commits?author=HanTY
contributorshttps://github.com/xinychen/transdim/graphs/contributors
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