Relevant bibliographies by topics / Spatiotemporal forecasting

Academic literature on the topic 'Spatiotemporal forecasting'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Spatiotemporal forecasting"

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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.

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Lin, 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.

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In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning.
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Oliveira, 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.

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The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV’s bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.
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Pavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling." Sensors 20, no. 23 (December 4, 2020): 6931. http://dx.doi.org/10.3390/s20236931.

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Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.
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Muñoz-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.

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COVID-19 has caused millions of infections and deaths over the last 2 years. 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 the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. 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 manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms of both root mean square error (RMSE) and explained variance (EV) when compared with previous models that have mainly focused on the temporal patterns and dependencies.
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., 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.

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López, 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.

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Ermagun, 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.

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Pavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting." Algorithms 13, no. 2 (February 13, 2020): 39. http://dx.doi.org/10.3390/a13020039.

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Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.
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Xiong, 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.

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Traffic flow forecasting is the essential part of intelligent transportation sSystem (ITS), which can fully protect traffic safety and improve traffic system management capability. Nevertheless, it is still a challenging problem, which is influenced by many complex factors, including regional distribution and external factors (e.g., holidays and weather). To combine various factors to forecast traffic flow, we presented a novel neural network structure called ConvLSTM-based Spatiotemporal Attention Network (CLSTAN). Specifically, our proposed model is composed of four modules: a preliminary feature extraction module, a spatial attention module, a temporal attention module, and an information fusion module. The spatiotemporal attention module can efficiently learn the complex spatiotemporal patterns of traffic flow through the attention mechanism. The spatial attention module uses a series of initial traffic flow maps as input and obtains the weights of the various regions through a ConvLSTM. The temporal attention module uses the spatially weighted traffic flow map as input and acquires the complex spatiotemporal patterns of traffic flow by a ConvLSTM that introduces an attention mechanism. Finally, the information fusion module integrates spatiotemporal information from multiple time dimensions to forecast future traffic flow. Moreover, to confirm the validity of our method, our experiments were conducted extensively on the TaxiBJ and BikeNYC datasets, and ultimately, CLSTAN performed better than other baseline experiments.
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Dissertations / Theses on the topic "Spatiotemporal forecasting"

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Fu, Kaiqun. "Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104165.

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The study of information extraction and knowledge exploration in the urban environment is gaining popularity. Ubiquitous sensors and a plethora of statistical reports provide an immense amount of heterogeneous urban data, such as traffic data, crime activity statistics, social media messages, and street imagery. The development of methods for heterogeneous urban data-based event identification and impacts analysis for a variety of event topics and assumptions is the subject of this dissertation. A graph convolutional neural network for crime prediction, a multitask learning system for traffic incident prediction with spatiotemporal feature learning, social media-based transportation event detection, and a graph convolutional network-based cyberbullying detection algorithm are the four methods proposed. Additionally, based on the sensitivity of these urban sensor data, a comprehensive discussion on ethical issues of urban computing is presented. This work makes the following contributions in urban perception predictions: 1) Create a preference learning system for inferring crime rankings from street view images using a bidirectional convolutional neural network (bCNN). 2) Propose a graph convolutional networkbased solution to the current urban crime perception problem; 3) Develop street view image retrieval algorithms to demonstrate real city perception. This work also makes the following contributions in traffic incident effect analysis: 1) developing a novel machine learning system for predicting traffic incident duration using temporal features; 2) modeling traffic speed similarity among road segments using spatial connectivity in feature space; and 3) proposing a sparse feature learning method for identifying groups of temporal features at a higher level. In transportation-related incidents detection, this work makes the following contributions: 1) creating a real-time social media-based traffic incident detection platform; 2) proposing a query expansion algorithm for traffic-related tweets; and 3) developing a text summarization tool for redundant traffic-related tweets. Cyberbullying detection from social media platforms is one of the major focus of this work: 1) Developing an online Dynamic Query Expansion process using concatenated keyword search. 2) Formulating a graph structure of tweet embeddings and implementing a Graph Convolutional Network for fine-grained cyberbullying classification. 3) Curating a balanced multiclass cyberbullying dataset from DQE, and making it publicly available. Additionally, 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 addressed.
Doctor 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.
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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.

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Wind power is one of the most promising renewable energy resources to replace conventional generation which carries high carbon footprints. Due to the abundance of wind and its relatively cheap installation costs, it is likely that wind power will become the most important energy resource in the near future. The successful development of wind power relies heavily on the ability to integrate wind power effciently into electricity grids. To optimize the value of wind power through careful power dispatches, techniques in forecasting the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. As wind is intermittent and wind turbines have non-linear power curves, this is a challenging task and many ongoing studies relate to the topic of wind power forecasting. For this reason, this thesis aims at contributing to the literature on wind power forecasting by constructing and analyzing various time series models and spatiotemporal models for wind power production. By exploring the key features of a portfolio of wind power data from Ireland and Denmark, we investigate different types of appropriate models. For instance, we develop anisotropic spatiotemporal correlation models to account for the propagation of weather fronts. We also develop twostage models to accommodate the probability masses that occur in wind power distributions due to chains of zeros. We apply the models to generate multi-step probability forecasts for both the individual and aggregated wind power using extensive data sets from Ireland and Denmark. From the evaluation of probability forecasts, valuable insights are obtained and deeper understanding of the strengths of various models could be applied to improve wind power forecasts in the future.
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Leasor, 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.

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Wei, 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.

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This research represents pioneering work to exploit new and rich data from tracking system to model player behaviour in sports. Novel methods for understanding and predicting player behaviour were proposed. The key contribution is the development of an algorithm that capture the “style” of players from trajectory data. Experimental results show improved prediction performance in various sports including tennis, basketball and soccer.
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Karimi, 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.

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Pologne, 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/.

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Thesis (M. S.)--Florida State University, 2005.
Advisor: 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.
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Book chapters on the topic "Spatiotemporal forecasting"

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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.

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Li, 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.

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Wang, 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.

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Pavlyuk, 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.

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Li, 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.

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Pathak, 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.

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Santipantakis, 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.

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Wang, 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.

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Yasuno, 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.

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Sahu, 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.

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Conference papers on the topic "Spatiotemporal forecasting"

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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.

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Wu, 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.

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Meng, 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.

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Spatiotemporal data aggregated over regions or time windows at various resolutions demonstrate heterogeneous patterns and dynamics in each resolution. Meanwhile, the multi-resolution characteristic provides rich contextual information, which is critical for effective long-sequence forecasting. The importance of such inter-resolution information is more significant in practical cases, where fine-grained data is usually collected via approaches with lower costs but also lower qualities compared to those for coarse-grained data. However, existing works focus on uni-resolution data and cannot be directly applied to fully utilize the aforementioned extra information in multi-resolution data. In this work, we propose Spatiotemporal Koopman Multi-Resolution Network (ST-KMRN), a physics-informed learning framework for long-sequence forecasting from multi-resolution spatiotemporal data. Our method jointly models data aggregated in multiple resolutions and captures the inter-resolution dynamics with the self-attention mechanism. We also propose downsampling and upsampling modules among resolutions to further strengthen the connections among data of multiple resolutions. Moreover, we enhance the modeling of intra-resolution dynamics with physics-informed modules based on Koopman theory. Experimental results demonstrate that our proposed approach achieves the best performance on the long-sequence forecasting tasks compared to baselines without a specific design for multi-resolution data.
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Fu, 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.

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Souto, 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.

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Deng, 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.

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Li, 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.

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Sawhney, 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.

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Pavlyuk, 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.

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Jenish, 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.

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Reports on the topic "Spatiotemporal forecasting"

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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.

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Vesselinov, 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.

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