Добірка наукової літератури з теми "Spatiotemporal forecasting"
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Статті в журналах з теми "Spatiotemporal forecasting"
Li, Cheng, Weimin Zheng, and Peng Ge. "Tourism demand forecasting with spatiotemporal features." Annals of Tourism Research 94 (May 2022): 103384. http://dx.doi.org/10.1016/j.annals.2022.103384.
Повний текст джерелаLin, Xu, Hongyue Wang, Qingqing Zhang, Chaolong Yao, Changxin Chen, Lin Cheng, and Zhaoxiong Li. "A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting." Remote Sensing 14, no. 7 (April 2, 2022): 1717. http://dx.doi.org/10.3390/rs14071717.
Повний текст джерелаOliveira, Mariana, Luís Torgo, and Vítor Santos Costa. "Evaluation Procedures for Forecasting with Spatiotemporal Data." Mathematics 9, no. 6 (March 23, 2021): 691. http://dx.doi.org/10.3390/math9060691.
Повний текст джерелаPavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling." Sensors 20, no. 23 (December 4, 2020): 6931. http://dx.doi.org/10.3390/s20236931.
Повний текст джерелаMuñoz-Organero, Mario, and Paula Queipo-Álvarez. "Deep Spatiotemporal Model for COVID-19 Forecasting." Sensors 22, no. 9 (May 5, 2022): 3519. http://dx.doi.org/10.3390/s22093519.
Повний текст джерела., V. Nourani, A. A. Moghaddam ., A. O. Nadiri ., and V. P. Singh . "Forecasting Spatiotemporal Water Levels of Tabriz Aquifer." Trends in Applied Sciences Research 3, no. 4 (April 1, 2008): 319–29. http://dx.doi.org/10.3923/tasr.2008.319.329.
Повний текст джерелаLópez, Cristóbal, Alberto Álvarez, and Emilio Hernández-García. "Forecasting Confined Spatiotemporal Chaos with Genetic Algorithms." Physical Review Letters 85, no. 11 (September 11, 2000): 2300–2303. http://dx.doi.org/10.1103/physrevlett.85.2300.
Повний текст джерелаErmagun, Alireza, and David Levinson. "Spatiotemporal traffic forecasting: review and proposed directions." Transport Reviews 38, no. 6 (March 6, 2018): 786–814. http://dx.doi.org/10.1080/01441647.2018.1442887.
Повний текст джерелаPavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting." Algorithms 13, no. 2 (February 13, 2020): 39. http://dx.doi.org/10.3390/a13020039.
Повний текст джерелаXiong, Liyan, Weihua Ding, Xiaohui Huang, and Weichun Huang. "CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting." Mathematical Problems in Engineering 2022 (July 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1604727.
Повний текст джерелаДисертації з теми "Spatiotemporal forecasting"
Fu, Kaiqun. "Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104165.
Повний текст джерелаDoctor 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.
Lau, 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.
Повний текст джерелаLeasor, 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.
Повний текст джерелаWei, 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.
Повний текст джерелаKarimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.
Повний текст джерелаPologne, Lawrence Cai Ming. "Spatiotemporal variability and prediction of rainfall over the eastern Caribbean." Diss., 2005. http://etd.lib.fsu.edu/theses/available/etd-07112005-162948/.
Повний текст джерелаAdvisor: Dr. Ming Cai, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Sept. 19, 2005). Document formatted into pages; contains x, 60 pages. Includes bibliographical references.
Частини книг з теми "Spatiotemporal forecasting"
Jiao, Xiaoying, and 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.
Повний текст джерелаLi, Zhigang, Margaret H. Dunham, and 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.
Повний текст джерелаWang, Rui, Robin Walters, and 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.
Повний текст джерелаPavlyuk, 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.
Повний текст джерелаLi, Zhigang, Liangang Liu, and 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.
Повний текст джерелаPathak, Jaideep, and 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.
Повний текст джерелаSantipantakis, Georgios M., Christos Doulkeridis, Akrivi Vlachou, and George A. Vouros. "Integrating Data by Discovering Topological and Proximity Relations Among Spatiotemporal Entities." In Big Data Analytics for Time-Critical Mobility Forecasting, 155–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45164-6_6.
Повний текст джерелаWang, Qiguang, Aixia Feng, Zhihai Zheng, and Guolin Feng. "Forecasting Numerical Model Errors Based on Spatiotemporal Optimized Scale Factors." In Recent Advances in Computer Science and Information Engineering, 611–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25778-0_85.
Повний текст джерелаYasuno, Takato, Akira Ishii, and Masazumi Amakata. "Rain-Code Fusion: Code-to-Code ConvLSTM Forecasting Spatiotemporal Precipitation." In Pattern Recognition. ICPR International Workshops and Challenges, 20–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68787-8_2.
Повний текст джерелаSahu, Sujit Kumar, Khandoker Shuvo Bakar, and Norhashidah Awang. "Bayesian forecasting using spatiotemporal models with applications to ozone concentration levels in the Eastern United States." In Geometry Driven Statistics, 260–81. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118866641.ch13.
Повний текст джерелаТези доповідей конференцій з теми "Spatiotemporal forecasting"
Zhao, Liang, Feng Chen, Chang-Tien Lu, and 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.
Повний текст джерелаWu, Dongxia, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, and 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.
Повний текст джерелаMeng, Chuizheng, Hao Niu, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, and Yan Liu. "Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/304.
Повний текст джерелаFu, Xingbo, Feng Gao, Jiang Wu, Xinyu Wei, and 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.
Повний текст джерелаSouto, Yania Molina, Fabio Porto, Ana Maria Moura, and 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.
Повний текст джерелаDeng, Songgaojun, Huzefa Rangwala, and 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.
Повний текст джерелаLi, Shaoying, Given Dan Meng, Wenhao Tao, Baiting Cui, Xu Zhu, and 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.
Повний текст джерелаSawhney, Ramit, Shivam Agarwal, Arnav Wadhwa, and 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.
Повний текст джерелаPavlyuk, 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.
Повний текст джерелаJenish, Justin, and 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.
Повний текст джерелаЗвіти організацій з теми "Spatiotemporal forecasting"
Allen-Dumas, Melissa, Kuldeep Kurte, Haowen Xu, Jibonananda Sanyal, and 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.
Повний текст джерелаVesselinov, Velimir, Richard Middleton, and 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.
Повний текст джерела