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Machine Learning for Spatiotemporal Sequence Forecasting: A Survey [article]

Xingjian Shi, Dit-Yan Yeung
2018 arXiv   pre-print
This survey aims to provide a systematic review of machine learning for STSF.  ...  Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem.  ...  In this paper, we review these machine learning based methods for spatiotemporal sequence forecasting.  ... 
arXiv:1808.06865v1 fatcat:3h42tl2szrbbfmrzvxnjyvslwu

Graph Neural Network for Traffic Forecasting: A Survey [article]

Weiwei Jiang, Jiayun Luo
2022 arXiv   pre-print
To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems.  ...  Traffic forecasting is important for the success of intelligent transportation systems.  ...  Machine learning (ML) and deep learning techniques have been introduced in this area to improve forecasting accuracy, for example, by modeling the whole city as a grid and applying a convolutional neural  ... 
arXiv:2101.11174v4 fatcat:txrrk6yia5dcvcamabhqahsrni

A Survey on Societal Event Forecasting with Deep Learning [article]

Songgaojun Deng, Yue Ning
2021 arXiv   pre-print
We first introduce how event forecasting problems are formulated as a machine learning prediction task.  ...  This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions.  ...  In contrast to previous work, this survey is structured around problems, data, and deep learning models for civil unrest and crime forecasting.  ... 
arXiv:2112.06345v1 fatcat:jtdlo67bbbazhj6xea55h6bbqa

Data-Centric Epidemic Forecasting: A Survey [article]

Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash
2022 arXiv   pre-print
as recent innovations in AI and machine learning.  ...  The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole.  ...  A survey on active learning and human-in-the-loop deep learning for medical image analysis.  ... 
arXiv:2207.09370v2 fatcat:x5a7uvmwrbgd7fiskufmn5nlmi

Deep Learning for Virus-Spreading Forecasting: a Brief Survey [article]

Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli, Riccardo Scheda, Michele Lombardi, Andrea Borghesi, Stefano Diciotti, Michela Milano
2021 arXiv   pre-print
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes.  ...  In this paper, we will outline the main Deep Learning approaches aimed at predicting the spreading of a disease in space and time.  ...  In the last years, we are witnessing a booming application of Machine Learning (ML) to create predictive models.  ... 
arXiv:2103.02346v1 fatcat:37trmamfszhalhxdqkji66gife

Spatiotemporal Data Mining: A Survey [article]

Arun Sharma, Zhe Jiang, Shashi Shekhar
2022 arXiv   pre-print
This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.  ...  Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining.  ...  ACKNOWLEDGMENTS We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.  ... 
arXiv:2206.12753v1 fatcat:jwm4mcxi5na7jbmbciwlrtt554

Rain-Code Fusion : Code-to-code ConvLSTM Forecasting Spatiotemporal Precipitation [article]

Takato Yasuno, Akira Ishii, Masazumi Amakata
2021 arXiv   pre-print
This paper proposes a rain-code approach for spatiotemporal precipitation code-to-code forecasting.  ...  Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for flood damage mitigation.  ...  We thank Takuji Fukumoto and Shinichi Kuramoto (MathWorks Japan) for providing us with MATLAB resources.  ... 
arXiv:2009.14573v6 fatcat:bu6xuwowzrg3jajpffl54sctuq

Physics-Guided Deep Learning for Dynamical Systems: A Survey [article]

Rui Wang, Rose Yu
2022 arXiv   pre-print
In this paper, we provide a structured overview of existing methodologies of integrating prior physical knowledge or physics-based modeling into DL, with a special emphasis on learning dynamical systems  ...  While deep learning (DL) provides novel alternatives for efficiently recognizing complex patterns and emulating nonlinear dynamics, its predictions do not necessarily obey the governing laws of physical  ...  Improving Generalization Generalization is a fundamental problem in machine learning.  ... 
arXiv:2107.01272v5 fatcat:k6hhdt6csnfebgkzrpuoeqkwzi

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
In this paper, we provide a comprehensive survey on recent progress in applying deep learning techniques for STDM.  ...  Then a framework is introduced to show a general pipeline of the utilization of deep learning models for STDM.  ...  For example, [30] proposed a Spatiotemporal Crime Network based on CNN to forecast the crime risk of each region in the urban area for the next day.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

A survey on visual analysis of ocean data

Cui Xie, Mingkui Li, Haoying wang, Junyu Dong
2019 Visual Informatics  
Finally, the opportunities are discussed for future studies.  ...  Firstly, this paper presents a basic concept of the ocean data produced from numerous measurement devices or computer simulations.  ...  They also would like to thank the anonymous reviewers for their comments that helped to improve the paper.  ... 
doi:10.1016/j.visinf.2019.08.001 fatcat:lnf2ixaegjhzjcz3tumnwhgjam

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting [article]

Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu
2018 arXiv   pre-print
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task.  ...  for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow.  ...  INTRODUCTION Spatiotemporal forecasting is a crucial task for a learning system that operates in a dynamic environment.  ... 
arXiv:1707.01926v3 fatcat:axu646j76ve5pppx5gkmtw2hba

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems [article]

Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora Salim
2021 arXiv   pre-print
Several available surveys capture STDM advances and report a wealth of important progress in this field.  ...  We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM.  ...  The work in [249] presented a review of machine learning methods for STDM sequence forecasting related problem. They focused on moving point cloud, regular grid, and irregular grids.  ... 
arXiv:2103.17128v1 fatcat:ci5pt5bytndr5inolznjsaizpi

Deep Spatiotemporal Model for COVID-19 Forecasting

Mario Muñoz-Organero, Paula Queipo-Álvarez
2022 Sensors  
Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize  ...  Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components.  ...  This paper has proposed a new machine learning model that learns both spatiotemporal patterns based on a sequence of COVID-19 incidence maps.  ... 
doi:10.3390/s22093519 pmid:35591208 pmcid:PMC9101138 fatcat:guvtcktxbng4xa3udjtv6gbjau

Subject Review:Weather Forecasting models that Employing Artificial Intelligence Approaches

Wedad Abdul Khuder Naser, Safana Hyder Abbas
2022 International Journal of Engineering Research and Advanced Technology  
It enables them to plan more precisely in the event of a natural disaster. It can help farmers make judgments about planting, for example.  ...  This research examines a number of weather forecasting models that employ artificial intelligence approaches.  ...  In 2020 The stone 3S2S (Spatiotemporal Convolutional Sequence to Sequence Network) architecture is a deep learning architecture that uses just convolutional layers to learn both spatial and temporal data  ... 
doi:10.31695/ijerat.2022.8.2.2 fatcat:43unhilwezantim3v7pyieql7m

Learning-Based Approaches for Graph Problems: A Survey [article]

Kai Siong Yow, Siqiang Luo
2022 arXiv   pre-print
In this survey, we provide a systematic review mainly on classic graph problems in which learning-based approaches have been proposed in addressing the problems.  ...  Recent studies have employed learning-based frameworks such as machine learning techniques in solving these problems, given that they are useful in discovering new patterns in structured data that can  ...  A relevant survey on AutoML can be found in [141] where the focus is on two major topics, hyperparameter optimisation (or tuning) and neural architecture for graph machine learning.  ... 
arXiv:2204.01057v2 fatcat:6oinpd56njcu5ait43327r6ege
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