Literatura científica selecionada sobre o tema "Classification and spatiotemporal forecasting"
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Artigos de revistas sobre o assunto "Classification and spatiotemporal forecasting"
Wang, Guosong, Xidong Wang, Xinrong Wu, Kexiu Liu, Yiquan Qi, Chunjian Sun e Hongli Fu. "A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting". Journal of Atmospheric and Oceanic Technology 39, n.º 3 (março de 2022): 285–301. http://dx.doi.org/10.1175/jtech-d-21-0043.1.
Texto completo da fontePlain, M. B., B. Minasny, A. B. McBratney e 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, n.º 3 (14 de maio de 2008): 1159–89. http://dx.doi.org/10.5194/hessd-5-1159-2008.
Texto completo da fonteJiang, Hongxun, Xiaotong Wang e Caihong Sun. "Predicting PM2.5 in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features". Atmosphere 13, n.º 11 (23 de outubro de 2022): 1744. http://dx.doi.org/10.3390/atmos13111744.
Texto completo da fonteYusro, Muhammad, e 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 de fevereiro de 2023): 107–17. http://dx.doi.org/10.4028/p-99od29.
Texto completo da fonteAkarsu, Osman Nuri. "A Bibliometric Review of Earthquake and Machine Learning Research". January 2024 5, n.º 1 (1 de abril de 2024): 1–10. http://dx.doi.org/10.36937/cebel.2024.1908.
Texto completo da fonteRotti, Sumanth, e Petrus C. Martens. "Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog". Astrophysical Journal Supplement Series 267, n.º 2 (1 de agosto de 2023): 40. http://dx.doi.org/10.3847/1538-4365/acdace.
Texto completo da fonteHushtan, Tetiana, e Anatoliy Kolodiychuk. "DEFINING CONDITIONS FOR INCREASING INNOVATION ACTIVITY IN THE INDUSTRIAL COMPLEX: ESSENCE, SYSTEMATIZATION, IDENTIFICATION". Baltic Journal of Economic Studies 7, n.º 4 (27 de setembro de 2021): 54–62. http://dx.doi.org/10.30525/2256-0742/2021-7-4-54-62.
Texto completo da fonteZhang, Yi, Fang Liu, Sheng Yue, Yuxuan Li e Qianwei Dong. "Accident Detection and Flow Prediction for Connected and Automated Transport Systems". Journal of Advanced Transportation 2023 (17 de abril de 2023): 1–9. http://dx.doi.org/10.1155/2023/5041509.
Texto completo da fonteKhokhlov, V., О. Umanska e I. Deriabina. "Objective classification of atmospheric processes for the East European region". Physical Geography and Geomorphology 90, n.º 2 (2018): 84–90. http://dx.doi.org/10.17721/phgg.2018.2.10.
Texto completo da fonteFossa, Manuel, Bastien Dieppois, Nicolas Massei, Matthieu Fournier, Benoit Laignel e Jean-Philippe Vidal. "Spatiotemporal and cross-scale interactions in hydroclimate variability: a case-study in France". Hydrology and Earth System Sciences 25, n.º 11 (4 de novembro de 2021): 5683–702. http://dx.doi.org/10.5194/hess-25-5683-2021.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteDeep 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.
Texto completo da fonteDoctor 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.
Texto completo da fonteLau, 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.
Texto completo da fonteLeasor, 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.
Texto completo da fonteRosswog, James. "Improving classification of spatiotemporal data using adaptive history filtering". Diss., Online access via UMI:, 2007.
Encontre o texto completo da fonteLo, Shin-Lian. "High-dimensional classification and attribute-based forecasting". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37193.
Texto completo da fonteHaensly, 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/.
Texto completo da fonteAlbanwan, Hessah AMYM. "Remote Sensing Image Enhancement through Spatiotemporal Filtering". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.
Texto completo da fonteWei, 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.
Texto completo da fonteLivros sobre o assunto "Classification and spatiotemporal forecasting"
Wisconsin. Dept. of Development. Division of Policy Development. Bureau of Research., ed. 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.
Encontre o texto completo da fonteWoo, Ming-Ko. Hydrological classification of Canadian prairie wetlands and prediction of wetland inundation in response to climatic variability. Ottawa: Canadian Wildlife Service, 1993.
Encontre o texto completo da fonteA, Kulikowski Casimir, ed. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. San Mateo, Calif: M. Kaufmann Publishers, 1991.
Encontre o texto completo da fonteCanada. Human Resources Development Canada., ed. Job futures. [Ottawa, ON: Human Resources Development Canada], 1996.
Encontre o texto completo da fonteLen'kov, Roman. Social forecasting and planning. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.
Texto completo da fonteNguyen, 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.
Encontre o texto completo da fonteZhuravlev, Yu I. Raspoznavanie, klassifikatsiya, prognoz: Matematicheskie metody i ikh primenie = Pattern recognition, classification, forecasting : mathematical techniques and their application, vol. 1. Moskva: Nauka, 1989.
Encontre o texto completo da fonteLabor, United States Department of. Occupational outlook handbook: 2008-2009. 2a ed. Indianapolis: Jist Pub., 2008.
Encontre o texto completo da fonteAlig, 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.
Encontre o texto completo da fonteAlig, 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.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Classification and spatiotemporal forecasting"
Jiao, Xiaoying, e Jason Li Chen. "Spatiotemporal econometric models". In Econometric Modelling and Forecasting of Tourism Demand, 126–43. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-6.
Texto completo da fonteMaithili, K., S. Leelavathy, G. Karthi e M. Adimoolam. "Spatiotemporal and Intelligent Transportation Forecasting". In Spatiotemporal Data Analytics and Modeling, 161–78. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9651-3_8.
Texto completo da fonteMetcalfe, Mike. "An Historical Classification". In Forecasting Profit, 51–62. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2255-3_3.
Texto completo da fonteIezzi, Domenica Fioredistella, e Maurizio Vichi. "Forecasting a Classification". In 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.
Texto completo da fonteLi, Zhigang, Margaret H. Dunham e Yongqiao Xiao. "STIFF: A Forecasting Framework for SpatioTemporal Data". In 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.
Texto completo da fonteWang, Rui, Robin Walters e Rose Yu. "Physics-Guided Deep Learning for Spatiotemporal Forecasting". In Knowledge-Guided Machine Learning, 179–210. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-8.
Texto completo da fontePavlyuk, Dmitry. "Spatiotemporal Forecasting of Urban Traffic Flow Volatility". In 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.
Texto completo da fonteLi, Zhigang, Liangang Liu e Margaret H. Dunham. "Considering Correlation between Variables to Improve Spatiotemporal Forecasting". In 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.
Texto completo da fonteKailas, Siva, Wenhao Luo e Katia Sycara. "Multi-robot Adaptive Sampling for Supervised Spatiotemporal Forecasting". In Progress in Artificial Intelligence, 349–61. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49008-8_28.
Texto completo da fontePathak, Jaideep, e Edward Ott. "Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems". In Natural Computing Series, 117–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-13-1687-6_6.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Classification and spatiotemporal forecasting"
Zhao, Liang, Feng Chen, Chang-Tien Lu e Naren Ramakrishnan. "Spatiotemporal Event Forecasting in Social Media". In 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.
Texto completo da fonteWu, Dongxia, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma e Rose Yu. "Quantifying Uncertainty in Deep Spatiotemporal Forecasting". In 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.
Texto completo da fonteFu, Xingbo, Feng Gao, Jiang Wu, Xinyu Wei e Fangwei Duan. "Spatiotemporal Attention Networks for Wind Power Forecasting". In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00032.
Texto completo da fonteSouto, Yania Molina, Fabio Porto, Ana Maria Moura e Eduardo Bezerra. "A Spatiotemporal Ensemble Approach to Rainfall Forecasting". In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489693.
Texto completo da fonteDeng, Songgaojun, Huzefa Rangwala e Yue Ning. "Robust Event Forecasting with Spatiotemporal Confounder Learning". In 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.
Texto completo da fonteChen, Yirong, Ziyue Li, Wanli Ouyang e Michael Lepech. "Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting". In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). IEEE, 2023. http://dx.doi.org/10.1109/case56687.2023.10260424.
Texto completo da fonteLi, Shaoying, Given Dan Meng, Wenhao Tao, Baiting Cui, Xu Zhu e Chao Kong. "Spatiotemporal Data Forecasting for Biological Invasion Detection". In 2021 7th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2021. http://dx.doi.org/10.1109/bigdia53151.2021.9619653.
Texto completo da fonteSawhney, Ramit, Shivam Agarwal, Arnav Wadhwa e Rajiv Ratn Shah. "Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting". In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00057.
Texto completo da fontePavlyuk, Dmitry. "Spatiotemporal Traffic Forecasting as a Video Prediction Problem". In 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.
Texto completo da fonteJenish, Justin, e M. Prabu. "A Neural Network Architecture for Spatiotemporal PM2.5 Forecasting". In 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). IEEE, 2022. http://dx.doi.org/10.1109/ic3sis54991.2022.9885669.
Texto completo da fonteRelatórios de organizações sobre o assunto "Classification and spatiotemporal forecasting"
Allen-Dumas, Melissa, Kuldeep Kurte, Haowen Xu, Jibonananda Sanyal e 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), abril de 2021. http://dx.doi.org/10.2172/1769653.
Texto completo da fonteVesselinov, Velimir, Richard Middleton e Carl Talsma. COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1774409.
Texto completo da fonteCook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree e 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.), outubro de 2022. http://dx.doi.org/10.21079/11681/45644.
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