Auswahl der wissenschaftlichen Literatur zum Thema „Classification and spatiotemporal forecasting“
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Zeitschriftenartikel zum Thema "Classification and spatiotemporal forecasting"
Wang, Guosong, Xidong Wang, Xinrong Wu, Kexiu Liu, Yiquan Qi, Chunjian Sun und Hongli Fu. „A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting“. Journal of Atmospheric and Oceanic Technology 39, Nr. 3 (März 2022): 285–301. http://dx.doi.org/10.1175/jtech-d-21-0043.1.
Der volle Inhalt der QuellePlain, M. B., B. Minasny, A. B. McBratney und 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, Nr. 3 (14.05.2008): 1159–89. http://dx.doi.org/10.5194/hessd-5-1159-2008.
Der volle Inhalt der QuelleJiang, Hongxun, Xiaotong Wang und Caihong Sun. „Predicting PM2.5 in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features“. Atmosphere 13, Nr. 11 (23.10.2022): 1744. http://dx.doi.org/10.3390/atmos13111744.
Der volle Inhalt der QuelleYusro, Muhammad, und Isnaini Nurisusilawati. „Forecasting Approach to Investigate Dynamic Growth of Organoid within 3D Matrix for Distinct Perspective“. Journal of Biomimetics, Biomaterials and Biomedical Engineering 59 (14.02.2023): 107–17. http://dx.doi.org/10.4028/p-99od29.
Der volle Inhalt der QuelleAkarsu, Osman Nuri. „A Bibliometric Review of Earthquake and Machine Learning Research“. January 2024 5, Nr. 1 (01.04.2024): 1–10. http://dx.doi.org/10.36937/cebel.2024.1908.
Der volle Inhalt der QuelleRotti, Sumanth, und Petrus C. Martens. „Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog“. Astrophysical Journal Supplement Series 267, Nr. 2 (01.08.2023): 40. http://dx.doi.org/10.3847/1538-4365/acdace.
Der volle Inhalt der QuelleHushtan, Tetiana, und Anatoliy Kolodiychuk. „DEFINING CONDITIONS FOR INCREASING INNOVATION ACTIVITY IN THE INDUSTRIAL COMPLEX: ESSENCE, SYSTEMATIZATION, IDENTIFICATION“. Baltic Journal of Economic Studies 7, Nr. 4 (27.09.2021): 54–62. http://dx.doi.org/10.30525/2256-0742/2021-7-4-54-62.
Der volle Inhalt der QuelleZhang, Yi, Fang Liu, Sheng Yue, Yuxuan Li und Qianwei Dong. „Accident Detection and Flow Prediction for Connected and Automated Transport Systems“. Journal of Advanced Transportation 2023 (17.04.2023): 1–9. http://dx.doi.org/10.1155/2023/5041509.
Der volle Inhalt der QuelleKhokhlov, V., О. Umanska und I. Deriabina. „Objective classification of atmospheric processes for the East European region“. Physical Geography and Geomorphology 90, Nr. 2 (2018): 84–90. http://dx.doi.org/10.17721/phgg.2018.2.10.
Der volle Inhalt der QuelleFossa, Manuel, Bastien Dieppois, Nicolas Massei, Matthieu Fournier, Benoit Laignel und Jean-Philippe Vidal. „Spatiotemporal and cross-scale interactions in hydroclimate variability: a case-study in France“. Hydrology and Earth System Sciences 25, Nr. 11 (04.11.2021): 5683–702. http://dx.doi.org/10.5194/hess-25-5683-2021.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleDeep 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.
Der volle Inhalt der QuelleDoctor 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.
Der volle Inhalt der QuelleLau, 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.
Der volle Inhalt der QuelleLeasor, 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.
Der volle Inhalt der QuelleRosswog, James. „Improving classification of spatiotemporal data using adaptive history filtering“. Diss., Online access via UMI:, 2007.
Den vollen Inhalt der Quelle findenLo, Shin-Lian. „High-dimensional classification and attribute-based forecasting“. Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37193.
Der volle Inhalt der QuelleHaensly, 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/.
Der volle Inhalt der QuelleAlbanwan, Hessah AMYM. „Remote Sensing Image Enhancement through Spatiotemporal Filtering“. The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.
Der volle Inhalt der QuelleWei, 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.
Der volle Inhalt der QuelleBücher zum Thema "Classification and spatiotemporal forecasting"
Wisconsin. Dept. of Development. Division of Policy Development. Bureau of Research., Hrsg. 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.
Den vollen Inhalt der Quelle findenWoo, Ming-Ko. Hydrological classification of Canadian prairie wetlands and prediction of wetland inundation in response to climatic variability. Ottawa: Canadian Wildlife Service, 1993.
Den vollen Inhalt der Quelle findenA, Kulikowski Casimir, Hrsg. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. San Mateo, Calif: M. Kaufmann Publishers, 1991.
Den vollen Inhalt der Quelle findenCanada. Human Resources Development Canada., Hrsg. Job futures. [Ottawa, ON: Human Resources Development Canada], 1996.
Den vollen Inhalt der Quelle findenLen'kov, Roman. Social forecasting and planning. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.
Der volle Inhalt der QuelleNguyen, 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.
Den vollen Inhalt der Quelle findenZhuravlev, Yu I. Raspoznavanie, klassifikatsiya, prognoz: Matematicheskie metody i ikh primenie = Pattern recognition, classification, forecasting : mathematical techniques and their application, vol. 1. Moskva: Nauka, 1989.
Den vollen Inhalt der Quelle findenLabor, United States Department of. Occupational outlook handbook: 2008-2009. 2. Aufl. Indianapolis: Jist Pub., 2008.
Den vollen Inhalt der Quelle findenAlig, 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.
Den vollen Inhalt der Quelle findenAlig, 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.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Classification and spatiotemporal forecasting"
Jiao, Xiaoying, und 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.
Der volle Inhalt der QuelleMaithili, K., S. Leelavathy, G. Karthi und 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.
Der volle Inhalt der QuelleMetcalfe, 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.
Der volle Inhalt der QuelleIezzi, Domenica Fioredistella, und 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.
Der volle Inhalt der QuelleLi, Zhigang, Margaret H. Dunham und 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.
Der volle Inhalt der QuelleWang, Rui, Robin Walters und 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.
Der volle Inhalt der QuellePavlyuk, 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.
Der volle Inhalt der QuelleLi, Zhigang, Liangang Liu und 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.
Der volle Inhalt der QuelleKailas, Siva, Wenhao Luo und 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.
Der volle Inhalt der QuellePathak, Jaideep, und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Classification and spatiotemporal forecasting"
Zhao, Liang, Feng Chen, Chang-Tien Lu und 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.
Der volle Inhalt der QuelleWu, Dongxia, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma und 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.
Der volle Inhalt der QuelleFu, Xingbo, Feng Gao, Jiang Wu, Xinyu Wei und 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.
Der volle Inhalt der QuelleSouto, Yania Molina, Fabio Porto, Ana Maria Moura und 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.
Der volle Inhalt der QuelleDeng, Songgaojun, Huzefa Rangwala und 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.
Der volle Inhalt der QuelleChen, Yirong, Ziyue Li, Wanli Ouyang und 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.
Der volle Inhalt der QuelleLi, Shaoying, Given Dan Meng, Wenhao Tao, Baiting Cui, Xu Zhu und 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.
Der volle Inhalt der QuelleSawhney, Ramit, Shivam Agarwal, Arnav Wadhwa und 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.
Der volle Inhalt der QuellePavlyuk, 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.
Der volle Inhalt der QuelleJenish, Justin, und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Classification and spatiotemporal forecasting"
Allen-Dumas, Melissa, Kuldeep Kurte, Haowen Xu, Jibonananda Sanyal und 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), April 2021. http://dx.doi.org/10.2172/1769653.
Der volle Inhalt der QuelleVesselinov, Velimir, Richard Middleton und Carl Talsma. COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1774409.
Der volle Inhalt der QuelleCook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree und 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.), Oktober 2022. http://dx.doi.org/10.21079/11681/45644.
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