Littérature scientifique sur le sujet « Classification and spatiotemporal forecasting »
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Articles de revues sur le sujet "Classification and spatiotemporal forecasting"
Wang, Guosong, Xidong Wang, Xinrong Wu, Kexiu Liu, Yiquan Qi, Chunjian Sun et Hongli Fu. « A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting ». Journal of Atmospheric and Oceanic Technology 39, no 3 (mars 2022) : 285–301. http://dx.doi.org/10.1175/jtech-d-21-0043.1.
Texte intégralPlain, M. B., B. Minasny, A. B. McBratney et R. W. Vervoort. « Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data ». Hydrology and Earth System Sciences Discussions 5, no 3 (14 mai 2008) : 1159–89. http://dx.doi.org/10.5194/hessd-5-1159-2008.
Texte intégralJiang, Hongxun, Xiaotong Wang et Caihong Sun. « Predicting PM2.5 in the Northeast China Heavy Industrial Zone : A Semi-Supervised Learning with Spatiotemporal Features ». Atmosphere 13, no 11 (23 octobre 2022) : 1744. http://dx.doi.org/10.3390/atmos13111744.
Texte intégralYusro, Muhammad, et Isnaini Nurisusilawati. « Forecasting Approach to Investigate Dynamic Growth of Organoid within 3D Matrix for Distinct Perspective ». Journal of Biomimetics, Biomaterials and Biomedical Engineering 59 (14 février 2023) : 107–17. http://dx.doi.org/10.4028/p-99od29.
Texte intégralAkarsu, Osman Nuri. « A Bibliometric Review of Earthquake and Machine Learning Research ». January 2024 5, no 1 (1 avril 2024) : 1–10. http://dx.doi.org/10.36937/cebel.2024.1908.
Texte intégralRotti, Sumanth, et Petrus C. Martens. « Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog ». Astrophysical Journal Supplement Series 267, no 2 (1 août 2023) : 40. http://dx.doi.org/10.3847/1538-4365/acdace.
Texte intégralHushtan, Tetiana, et Anatoliy Kolodiychuk. « DEFINING CONDITIONS FOR INCREASING INNOVATION ACTIVITY IN THE INDUSTRIAL COMPLEX : ESSENCE, SYSTEMATIZATION, IDENTIFICATION ». Baltic Journal of Economic Studies 7, no 4 (27 septembre 2021) : 54–62. http://dx.doi.org/10.30525/2256-0742/2021-7-4-54-62.
Texte intégralZhang, Yi, Fang Liu, Sheng Yue, Yuxuan Li et Qianwei Dong. « Accident Detection and Flow Prediction for Connected and Automated Transport Systems ». Journal of Advanced Transportation 2023 (17 avril 2023) : 1–9. http://dx.doi.org/10.1155/2023/5041509.
Texte intégralKhokhlov, V., О. Umanska et I. Deriabina. « Objective classification of atmospheric processes for the East European region ». Physical Geography and Geomorphology 90, no 2 (2018) : 84–90. http://dx.doi.org/10.17721/phgg.2018.2.10.
Texte intégralFossa, Manuel, Bastien Dieppois, Nicolas Massei, Matthieu Fournier, Benoit Laignel et Jean-Philippe Vidal. « Spatiotemporal and cross-scale interactions in hydroclimate variability : a case-study in France ». Hydrology and Earth System Sciences 25, no 11 (4 novembre 2021) : 5683–702. http://dx.doi.org/10.5194/hess-25-5683-2021.
Texte intégralThèses sur le sujet "Classification and spatiotemporal forecasting"
Kirchmeyer, Matthieu. « Out-of-distribution Generalization in Deep Learning : Classification and Spatiotemporal Forecasting ». Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS080.
Texte intégralDeep learning has emerged as a powerful approach for modelling static data like images and more recently for modelling dynamical systems like those underlying times series, videos or physical phenomena. Yet, neural networks were observed to not generalize well outside the training distribution, in other words out-of-distribution. This lack of generalization limits the deployment of deep learning in autonomous systems or online production pipelines, which are faced with constantly evolving data. In this thesis, we design new strategies for out-of-distribution generalization. These strategies handle the specific challenges posed by two main application tasks, classification of static data and spatiotemporal dynamics forecasting. The first two parts of this thesis consider the classification problem. We first investigate how we can efficiently leverage some observed training data from a target domain for adaptation. We then explore how to generalize to unobserved domains without access to such data. The last part of this thesis handles various generalization problems specific to spatiotemporal forecasting
Fu, Kaiqun. « Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors ». Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104165.
Texte intégralDoctor of Philosophy
The ubiquitously deployed urban sensors such as traffic speed meters, street-view cameras, and even smartphones in everybody's pockets are generating terabytes of data every hour. How do we refine the valuable intelligence out of such explosions of urban data and information became one of the profitable questions in the field of data mining and urban computing. In this dissertation, four innovative applications are proposed to solve real-world problems with big data of the urban sensors. In addition, the foreseeable ethical vulnerabilities in the research fields of urban computing and event predictions are addressed. The first work explores the connection between urban perception and crime inferences. StreetNet is proposed to learn crime rankings from street view images. This work presents the design of a street view images retrieval algorithm to improve the representation of urban perception. A data-driven, spatiotemporal algorithm is proposed to find unbiased label mappings between the street view images and the crime ranking records. The second work proposes a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework. Such functionality provided by this model is helpful for the transportation operators and first responders to judge the influences of traffic incidents. In the third work, a social media-based traffic status monitoring system is established. The system is initiated by a transportation-related keyword generation process. A state-of-the-art tweets summarization algorithm is designed to eliminate the redundant tweets information. In addition, we show that the proposed tweets query expansion algorithm outperforms the previous methods. The fourth work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim's age, ethnicity, gender, religion, or other quality. This work represents a step forward for establishing an active anti-cyberbullying presence in social media and a step forward towards a future without cyberbullying. Finally, a discussion of the ethical issues in the urban computing community is addressed. This work seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are pointed out.
Khalid, Shehzad. « Motion classification using spatiotemporal approximation of object trajectories ». Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492915.
Texte intégralLau, Ada. « Probabilistic wind power forecasts : from aggregated approach to spatiotemporal models ». Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f5a66568-baac-4f11-ab1e-dc79061cfb0f.
Texte intégralLeasor, Zachary T. « Spatiotemporal Variations of Drought Persistence in the South-Central United States ». The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497444478957738.
Texte intégralRosswog, James. « Improving classification of spatiotemporal data using adaptive history filtering ». Diss., Online access via UMI:, 2007.
Trouver le texte intégralLo, Shin-Lian. « High-dimensional classification and attribute-based forecasting ». Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37193.
Texte intégralHaensly, Paul J. « The Application of Statistical Classification to Business Failure Prediction ». Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc278187/.
Texte intégralAlbanwan, Hessah AMYM. « Remote Sensing Image Enhancement through Spatiotemporal Filtering ». The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.
Texte intégralWei, Xinyu. « Modelling and predicting adversarial behaviour using large amounts of spatiotemporal data ». Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/101959/1/Xinyu_Wei_Thesis.pdf.
Texte intégralLivres sur le sujet "Classification and spatiotemporal forecasting"
Wisconsin. Dept. of Development. Division of Policy Development. Bureau of Research., dir. Employment potential of Wisconsin industry groups : An analysis and industry classification. Madison, Wis : Wisconsin Dept. of Development, Division of Policy Development, Bureau of Research, 1985.
Trouver le texte intégralWoo, Ming-Ko. Hydrological classification of Canadian prairie wetlands and prediction of wetland inundation in response to climatic variability. Ottawa : Canadian Wildlife Service, 1993.
Trouver le texte intégralA, Kulikowski Casimir, dir. Computer systems that learn : Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. San Mateo, Calif : M. Kaufmann Publishers, 1991.
Trouver le texte intégralCanada. Human Resources Development Canada., dir. Job futures. [Ottawa, ON : Human Resources Development Canada], 1996.
Trouver le texte intégralLen'kov, Roman. Social forecasting and planning. ru : INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.
Texte intégralNguyen, Van O. Analysis of the U.S. Marine Corps' steady state Markov model for forecasting annual first-term enlisted classification requirements. Monterey, Calif : Naval Postgraduate School, 1997.
Trouver le texte intégralZhuravlev, Yu I. Raspoznavanie, klassifikatsiya, prognoz : Matematicheskie metody i ikh primenie = Pattern recognition, classification, forecasting : mathematical techniques and their application, vol. 1. Moskva : Nauka, 1989.
Trouver le texte intégralLabor, United States Department of. Occupational outlook handbook : 2008-2009. 2e éd. Indianapolis : Jist Pub., 2008.
Trouver le texte intégralAlig, Ralph J. Area changes for forest cover types in the United States, 1952 to 1997, with projections to 2050. Portland, Or : Pacific Northwest Research Station, 2004.
Trouver le texte intégralAlig, Ralph J. Area changes for forest cover types in the United States, 1952 to 1997, with projections to 2050. [Portland, OR] : U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2004.
Trouver le texte intégralChapitres de livres sur le sujet "Classification and spatiotemporal forecasting"
Jiao, Xiaoying, et Jason Li Chen. « Spatiotemporal econometric models ». Dans Econometric Modelling and Forecasting of Tourism Demand, 126–43. London : Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-6.
Texte intégralMaithili, K., S. Leelavathy, G. Karthi et M. Adimoolam. « Spatiotemporal and Intelligent Transportation Forecasting ». Dans Spatiotemporal Data Analytics and Modeling, 161–78. Singapore : Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9651-3_8.
Texte intégralMetcalfe, Mike. « An Historical Classification ». Dans Forecasting Profit, 51–62. Boston, MA : Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2255-3_3.
Texte intégralIezzi, Domenica Fioredistella, et Maurizio Vichi. « Forecasting a Classification ». Dans Studies in Classification, Data Analysis, and Knowledge Organization, 27–34. Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60126-2_4.
Texte intégralLi, Zhigang, Margaret H. Dunham et Yongqiao Xiao. « STIFF : A Forecasting Framework for SpatioTemporal Data ». Dans Mining Multimedia and Complex Data, 183–98. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39666-6_12.
Texte intégralWang, Rui, Robin Walters et Rose Yu. « Physics-Guided Deep Learning for Spatiotemporal Forecasting ». Dans Knowledge-Guided Machine Learning, 179–210. Boca Raton : Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-8.
Texte intégralPavlyuk, Dmitry. « Spatiotemporal Forecasting of Urban Traffic Flow Volatility ». Dans Lecture Notes in Networks and Systems, 63–72. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68476-1_6.
Texte intégralLi, Zhigang, Liangang Liu et Margaret H. Dunham. « Considering Correlation between Variables to Improve Spatiotemporal Forecasting ». Dans Advances in Knowledge Discovery and Data Mining, 519–31. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36175-8_52.
Texte intégralKailas, Siva, Wenhao Luo et Katia Sycara. « Multi-robot Adaptive Sampling for Supervised Spatiotemporal Forecasting ». Dans Progress in Artificial Intelligence, 349–61. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49008-8_28.
Texte intégralPathak, Jaideep, et Edward Ott. « Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems ». Dans Natural Computing Series, 117–38. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-13-1687-6_6.
Texte intégralActes de conférences sur le sujet "Classification and spatiotemporal forecasting"
Zhao, Liang, Feng Chen, Chang-Tien Lu et Naren Ramakrishnan. « Spatiotemporal Event Forecasting in Social Media ». Dans Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA : Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.108.
Texte intégralWu, Dongxia, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma et Rose Yu. « Quantifying Uncertainty in Deep Spatiotemporal Forecasting ». Dans KDD '21 : The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3447548.3467325.
Texte intégralFu, Xingbo, Feng Gao, Jiang Wu, Xinyu Wei et Fangwei Duan. « Spatiotemporal Attention Networks for Wind Power Forecasting ». Dans 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00032.
Texte intégralSouto, Yania Molina, Fabio Porto, Ana Maria Moura et Eduardo Bezerra. « A Spatiotemporal Ensemble Approach to Rainfall Forecasting ». Dans 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489693.
Texte intégralDeng, Songgaojun, Huzefa Rangwala et Yue Ning. « Robust Event Forecasting with Spatiotemporal Confounder Learning ». Dans KDD '22 : The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3534678.3539427.
Texte intégralChen, Yirong, Ziyue Li, Wanli Ouyang et Michael Lepech. « Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting ». Dans 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). IEEE, 2023. http://dx.doi.org/10.1109/case56687.2023.10260424.
Texte intégralLi, Shaoying, Given Dan Meng, Wenhao Tao, Baiting Cui, Xu Zhu et Chao Kong. « Spatiotemporal Data Forecasting for Biological Invasion Detection ». Dans 2021 7th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2021. http://dx.doi.org/10.1109/bigdia53151.2021.9619653.
Texte intégralSawhney, Ramit, Shivam Agarwal, Arnav Wadhwa et Rajiv Ratn Shah. « Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting ». Dans 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00057.
Texte intégralPavlyuk, Dmitry. « Spatiotemporal Traffic Forecasting as a Video Prediction Problem ». Dans 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2019. http://dx.doi.org/10.1109/mtits.2019.8883353.
Texte intégralJenish, Justin, et M. Prabu. « A Neural Network Architecture for Spatiotemporal PM2.5 Forecasting ». Dans 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). IEEE, 2022. http://dx.doi.org/10.1109/ic3sis54991.2022.9885669.
Texte intégralRapports d'organisations sur le sujet "Classification and spatiotemporal forecasting"
Allen-Dumas, Melissa, Kuldeep Kurte, Haowen Xu, Jibonananda Sanyal et Guannan Zhang. A Spatiotemporal Sequence Forecasting Platform to Advance the Predictionof Changing Spatiotemporal Patterns of CO2 Concentrationby Incorporating Human Activity and Hydrological Extremes. Office of Scientific and Technical Information (OSTI), avril 2021. http://dx.doi.org/10.2172/1769653.
Texte intégralVesselinov, Velimir, Richard Middleton et Carl Talsma. COVID-19 : Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting. Office of Scientific and Technical Information (OSTI), avril 2021. http://dx.doi.org/10.2172/1774409.
Texte intégralCook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree et Ronald Treminio. Landform identification in the Chihuahuan Desert for dust source characterization applications : developing a landform reference data set. Engineer Research and Development Center (U.S.), octobre 2022. http://dx.doi.org/10.21079/11681/45644.
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