Relevant bibliographies by topics / Classification and spatiotemporal forecasting

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Classification and spatiotemporal forecasting'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Classification and spatiotemporal forecasting"

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Wang, Guosong, Xidong Wang, Xinrong Wu, Kexiu Liu, Yiquan Qi, Chunjian Sun, and Hongli Fu. "A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting." Journal of Atmospheric and Oceanic Technology 39, no. 3 (March 2022): 285–301. http://dx.doi.org/10.1175/jtech-d-21-0043.1.

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Abstract The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies.
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Plain, M. B., B. Minasny, A. B. McBratney, and 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 (May 14, 2008): 1159–89. http://dx.doi.org/10.5194/hessd-5-1159-2008.

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Abstract. A major limitation of statistical forecasts for specific weather station sites is that they are not spatial in the true sense. And while spatial predictions have been studied, their results have indicated a lack of seasonality. Global Circulation Models (GCMs) are spatial, but their spatial resolution is rather coarse. Here we propose spatially explicit seasonal forecasting, based on the Fuzzy Classification of long-term (40 years) daily rainfall and temperature data to create climate memberships over time and location. Data were obtained from weather stations across south-east Australia, covering sub-tropical to arid climate zones. Class memberships were used to produce seasonal predictions using correlations with climate drivers and a regression rules approach. Therefore, this model includes both local climate feedback and the continental drivers. The developed seasonal forecasting model predicts rainfall and temperature reasonably accurately. The final 6-month forecast for average maximum temperature and rainfall produced relative errors of 0.89 and 0.56 and Pearson correlation coefficients of 0.83 and 0.82, respectively.
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Jiang, Hongxun, Xiaotong Wang, and Caihong Sun. "Predicting PM2.5 in the Northeast China Heavy Industrial Zone: A Semi-Supervised Learning with Spatiotemporal Features." Atmosphere 13, no. 11 (October 23, 2022): 1744. http://dx.doi.org/10.3390/atmos13111744.

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Particulate matter PM2.5 pollution affects the Chinese population, particularly in cities such as Shenyang in northeastern China, which occupies a number of traditional heavy industries. This paper proposes a semi-supervised learning model used for predicting PM2.5 concentrations. The model incorporates rich data from the real world, including 11 air quality monitoring stations in Shenyang and nearby cities. There are three types of data: air monitoring, meteorological data, and spatiotemporal information (such as the spatiotemporal effects of PM2.5 emissions and diffusion across different geographical regions). The model consists of two classifiers: genetic programming (GP) to forecast PM2.5 concentrations and support vector classification (SVC) to predict trends. The experimental results show that the proposed model performs better than baseline models in accuracy, including 3% to 18% over a classic multivariate linear regression (MLR), 1% to 11% over a multi-layer perceptron neural network (MLP-ANN), and 21% to 68% over a support vector regression (SVR). Furthermore, the proposed GP approach provides an intuitive contribution analysis of factors for PM2.5 concentrations. The data of backtracking points adjacent to other monitoring stations are critical in forecasting shorter time intervals (1 h). Wind speeds are more important in longer intervals (6 and 24 h).
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Yusro, Muhammad, and Isnaini Nurisusilawati. "Forecasting Approach to Investigate Dynamic Growth of Organoid within 3D Matrix for Distinct Perspective." Journal of Biomimetics, Biomaterials and Biomedical Engineering 59 (February 14, 2023): 107–17. http://dx.doi.org/10.4028/p-99od29.

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Organoid as a 3D structured model in vitro has difficulty in controlling its size. This issue becomes problematic when it is applied in a microfluidic source and sink-based because different dimension leads to different exposure to morphogen resulting in different cell fate. As a model used for biomedical purposes, this problem could lead to a discrepancy. This research is imposed to implement the forecasting method to study the dynamic of organoid growth profile. This approach could help a better understanding via spatiotemporal perspective complemented with a mathematical formula. The forecasting approach that clarifies the trend of this organoid growth by assessing whether the decided trend fits in every (or particular) stage (or not) has not been informed yet. Neural tube organoids have four different mechanical stiffness (0,5 kPa, 2 kPa, 4 kPa, 8kPa) which are documented in three days by time-lapse microscopy used in this experiment. These objects are mapped in a spatiotemporal fashion investigated in the profile and assessed by exponential trend. The actual phenomenon and forecasted result are evaluated by Mean Absolute Percentage Error (MAPE). Based on the result, the profile of organoid growth indicates that the organoid develops mostly following an exponential profile with the highest R2 value of 0,9868 and the lowest being 0,8734. Based on the MAPE value calculation it could be confirmed that the MAPE value on day 3 is the highest among the others indicating that the extended time of growth tends to have a different profile rather than the exponential trend after day 2. It should be noted that on the lowest stiffness (0,5 kPa) the mechanical properties do not significantly affect the organoid size during the development. Almost all (11 by 12 data or 91,6%) of the MAPE value is in excellent criteria (the value is less than 10%). Only one data does not belong to that classification which is in 8 kPa on day 3. Indicating that the higher stiffness the stronger effect on the system. From the axis development perspective, the organoid does not follow any specific pattern. This research could be a reference for a better understanding of the organoid growth profile in the 3D matrix environment which is nowadays become a hot topic in biomedical applications.
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Akarsu, Osman Nuri. "A Bibliometric Review of Earthquake and Machine Learning Research." January 2024 5, no. 1 (April 1, 2024): 1–10. http://dx.doi.org/10.36937/cebel.2024.1908.

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This article presents a bibliometric review of earthquake research and its integration with machine learning techniques. Over the past two decades, there has been a growing interest in using machine learning to enhance earthquake prediction and research. The review collected 1172 scholarly articles from the Web of Science database, focusing on the keywords "earthquake" and "machine learning." Machine learning has shown promise in improving earthquake forecasting models and aiding decision-making in disaster management, infrastructure design, and emergency response. However, it is noted that the application of machine learning in earthquake engineering is still in its early stages and requires further exploration. Key findings of this review include the increasing importance of certain keywords in earthquake and machine learning research, such as "prediction," "neural network," "classification," "logistic regression," and "performance." These keywords highlight the central areas of research focus within this field. The review also identifies research trends and gaps, including the need for more exploration of large-scale, high-dimensional, nonlinear, non-stationary, and heterogeneous spatiotemporal data in earthquake engineering. It emphasizes the necessity for novel machine learning algorithms tailored specifically for earthquake prediction and analysis. Furthermore, it highlights the need for addressing uncertainty in earthquake research and improving forecasting models. The review underscores the growth in interest and collaboration in earthquake research and machine learning, evident in the increasing number of scholarly contributions over the years. In summary, this bibliometric review highlights the importance of accurate forecasting and the potential of machine learning techniques in advancing this field.
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Rotti, Sumanth, and Petrus C. Martens. "Analysis of SEP Events and Their Possible Precursors Based on the GSEP Catalog." Astrophysical Journal Supplement Series 267, no. 2 (August 1, 2023): 40. http://dx.doi.org/10.3847/1538-4365/acdace.

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Abstract Solar energetic particle (SEP) events are one of the most crucial aspects of space weather. Their prediction depends on various factors including the source solar eruptions such as flares and coronal mass ejections (CMEs). The Geostationary Solar Energetic Particle (GSEP) events catalog was developed as an extensive data set toward this effort for solar cycles 22, 23, and 24. In the present work, we review and extend the GSEP data set by (1) adding “weak” SEP events that have proton enhancements from 0.5 to 10 pfu in the E >10 MeV channel and (2) improving the associated solar source eruptions information. We analyze and discuss spatiotemporal properties such as flare magnitudes, locations, rise times, and speeds and widths of CMEs. We check for the correlation of these parameters with peak proton fluxes and event fluences. Our study also focuses on understanding feature importance toward the optimal performance of machine-learning (ML) models for SEP event forecasting. We implement random forest, extreme gradient boosting, logistic regression, and support vector machine classifiers in a binary classification schema. Based on the evaluation of our best models, we find both the flare and CME parameters are requisites to predict the occurrence of an SEP event. This work is a foundation for our further efforts on SEP event forecasting using robust ML methods.
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Hushtan, Tetiana, and Anatoliy Kolodiychuk. "DEFINING CONDITIONS FOR INCREASING INNOVATION ACTIVITY IN THE INDUSTRIAL COMPLEX: ESSENCE, SYSTEMATIZATION, IDENTIFICATION." Baltic Journal of Economic Studies 7, no. 4 (September 27, 2021): 54–62. http://dx.doi.org/10.30525/2256-0742/2021-7-4-54-62.

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The subject of the study is to substantiate classifications of the factors of innovation development of the industry: according to the priority, traditional, barrier, according to the hierarchical level of innovation, the nature of supply demand for innovation, the peculiarity of the influence of factors on the market environment, the influence of factors on innovation localization, importance of innovations, the effect of innovation, nature of the impact, the power of influence, the type of competition, and other classifications of factors of innovation development of the industry. The need to intensify the development of Ukrainian industry in an innovative way requires the identification of the impact on these processes of various factors. To group these influences, the assessment of these factors should be done in the context of separate classes. For this purpose, it is necessary to develop a classification of innovative factors of industrial development. The purpose of the paper is to investigate and systematize the defining conditions for the activation of innovative development in the industrial sphere. The following methods were used in the work: dialectical method of scientific knowledge, analysis and synthesis, comparative, as well as the method of data generalization. It is proved that the complex non-use of these classifications for the substantiation of innovative development of the industry will improve the quality of planning and forecasting documentation and provisions of industrial policy. The applied meaning arising from the criteria for the classification of factors is based on their specific spatiotemporal and situational application, in particular, in conditions of imperfect competition. The classification of innovative factors of industrial development according to their priority is given. In this classification, the priority is determined by the importance and relevance of innovative industry development tasks on the basis of conclusions made as a result of the literature review. Summarizing the factors of innovation development in the barrier classification allows us to distinguish three aggregated groups of factors: socio-political and managerial, socio-economic, and financial. Our socio-economic analysis of innovative development factors of industry also allowed us to identify the following their classification attributes: the hierarchical level of innovation implementation, the character of demand for innovation, the nature of the impact on the market environment, the type of impact, the time horizon of action, impact on the area of innovation localization, the economic essence of innovation, the nature of the significance of innovation, innovation effect, the nature of effective impact, the power of influence, the type of competition.
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Zhang, Yi, Fang Liu, Sheng Yue, Yuxuan Li, and Qianwei Dong. "Accident Detection and Flow Prediction for Connected and Automated Transport Systems." Journal of Advanced Transportation 2023 (April 17, 2023): 1–9. http://dx.doi.org/10.1155/2023/5041509.

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Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management.
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Khokhlov, V., О. Umanska, and 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.

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The article describes the objective classification, involving the automated systems application to section the atmospheric processes by types. The objective of typing is to split a collection of objects of a certain sample according to the maximum-distance-separable groups. The basis for objective classification includes several methods: correlation, cluster analysis, nonlinear methods, neural network method, etc. One of the analysis methods for the characteristics of synoptic processes is typing, or the classification of synoptic processes by types, which allows finding common features of development of atmospheric processes in a large variety of synoptic situations. The objective of typing is to split a collection of objects of a certain sample by maximum-distance-separable groups. Since the beginning of the XIX century, when the classification of synoptic processes was introduced to the practice of weather forecasting, there were published a large number of works that differ in specific methodological approaches, in a number of selected types of weather, etc. Currently, only on the territory of Europe, according to various estimates, researchers allocate from 4 to 40 types of atmospheric processes and account for up to 209 subtypes, 84 % of which is obtained by analyzing the data of surface atmospheric pressure, geopotential heights and wind characteristics. On-scale data from 6 to 12 hours (9 %), daily (84 %) and monthly data(7 %) are used as an output information. The spatial range varies from mesoscale (5% of classifications), regional (3 %), on an individual nationwide scale (20 %), as part of the continent (22 %) and the continent as a whole (50 %) The second half of the XX century and the beginning of XXI century are characterized by high rates of changes in climatic and circulation conditions. An occurrence of rare weather extremums is a manifestation of the transition state of the atmosphere and its instability. Often regional changes have more significant variations than global. Therefore, progress, in the understanding of current trends of climate change, is impossible without taking into account spatiotemporal dynamics of atmospheric processes. The author considers the main principles of GWL classification and investigates regional characteristics of synoptic processes in the territory of Europe based on the characteristics of the surface baric field and displacement trajectories of the main baric systems. The purpose of this paper is to explore one of the most popular classifications for the European region and to establish the possibility of its further application to the territory of Ukraine. Research methods: a statistical description of the synoptic types for Europe for the period from September 1957 up to August 2002. Results of the study confirm the fact, that the addressed classification is aimed at creation of seasonal and interannual forecasts of synoptic processes and works better in the central, western and southern directions of Europe.
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Fossa, Manuel, Bastien Dieppois, Nicolas Massei, Matthieu Fournier, Benoit Laignel, and Jean-Philippe Vidal. "Spatiotemporal and cross-scale interactions in hydroclimate variability: a case-study in France." Hydrology and Earth System Sciences 25, no. 11 (November 4, 2021): 5683–702. http://dx.doi.org/10.5194/hess-25-5683-2021.

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Abstract. Understanding how water resources vary in response to climate at different temporal and spatial scales is crucial to inform long-term management. Climate change impacts and induced trends may indeed be substantially modulated by low-frequency (multi-year) variations, whose strength varies in time and space, with large consequences for risk forecasting systems. In this study, we present a spatial classification of precipitation, temperature, and discharge variability in France, based on a fuzzy clustering and wavelet spectra of 152 near-natural watersheds between 1958 and 2008. We also explore phase–phase and phase–amplitude causal interactions between timescales of each homogeneous region. A total of three significant timescales of variability are found in precipitation, temperature, and discharge, i.e., 1, 2–4, and 5–8 years. The magnitude of these timescales of variability is, however, not constant over the different regions. For instance, southern regions are markedly different from other regions, with much lower (5–8 years) variability and much larger (2–4 years) variability. Several temporal changes in precipitation, temperature, and discharge variability are identified during the 1980s and 1990s. Notably, in the southern regions of France, we note a decrease in annual temperature variability in the mid 1990s. Investigating cross-scale interactions, our study reveals causal and bi-directional relationships between higher- and lower-frequency variability, which may feature interactions within the coupled land–ocean–atmosphere systems. Interestingly, however, even though time frequency patterns (occurrence and timing of timescales of variability) were similar between regions, cross-scale interactions are far much complex, differ between regions, and are not systematically transferred from climate (precipitation and temperature) to hydrological variability (discharge). Phase–amplitude interactions are indeed absent in discharge variability, although significant phase–amplitude interactions are found in precipitation and temperature. This suggests that watershed characteristics cancel the negative feedback systems found in precipitation and temperature. This study allows for a multi-timescale representation of hydroclimate variability in France and provides unique insight into the complex nonlinear dynamics of this variability and its predictability.
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Dissertations / Theses on the topic "Classification and spatiotemporal forecasting"

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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.

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L’apprentissage profond a émergé comme une approche puissante pour la modélisation de données statiques comme les images et, plus récemment, pour la modélisation de systèmes dynamiques comme ceux sous-jacents aux séries temporelles, aux vidéos ou aux phénomènes physiques. Cependant, les réseaux neuronaux ne généralisent pas bien en dehors de la distribution d’apprentissage, en d’autres termes, hors-distribution. Ceci limite le déploiement de l’apprentissage profond dans les systèmes autonomes ou les systèmes de production en ligne, qui sont confrontés à des données en constante évolution. Dans cette thèse, nous concevons de nouvelles stratégies d’apprentissage pour la généralisation hors-distribution. Celles-ci tiennent compte des défis spécifiques posés par deux tâches d’application principales, la classification de données statiques et la prévision de dynamiques spatiotemporelles. Les deux premières parties de cette thèse étudient la classification. Nous présentons d’abord comment utiliser des données d’entraînement en quantité limitée d’un domaine cible pour l’adaptation. Nous explorons ensuite comment généraliser à des domaines non observés sans accès à de telles données. La dernière partie de cette thèse présente diverses tâches de généralisation, spécifiques à la prévision spatiotemporelle
Deep 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
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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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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.

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Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This thesis presents a novel technique for clustering and classification of object trajectory based video motion clips using basis function approximation.
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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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Rosswog, James. "Improving classification of spatiotemporal data using adaptive history filtering." Diss., Online access via UMI:, 2007.

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Lo, Shin-Lian. "High-dimensional classification and attribute-based forecasting." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37193.

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This thesis consists of two parts. The first part focuses on high-dimensional classification problems in microarray experiments. The second part deals with forecasting problems with a large number of categories in predictors. Classification problems in microarray experiments refer to discriminating subjects with different biologic phenotypes or known tumor subtypes as well as to predicting the clinical outcomes or the prognostic stages of subjects. One important characteristic of microarray data is that the number of genes is much larger than the sample size. The penalized logistic regression method is known for simultaneous variable selection and classification. However, the performance of this method declines as the number of variables increases. With this concern, in the first study, we propose a new classification approach that employs the penalized logistic regression method iteratively with a controlled size of gene subsets to maintain variable selection consistency and classification accuracy. The second study is motivated by a modern microarray experiment that includes two layers of replicates. This new experimental setting causes most existing classification methods, including penalized logistic regression, not appropriate to be directly applied because the assumption of independent observations is violated. To solve this problem, we propose a new classification method by incorporating random effects into penalized logistic regression such that the heterogeneity among different experimental subjects and the correlations from repeated measurements can be taken into account. An efficient hybrid algorithm is introduced to tackle computational challenges in estimation and integration. Applications to a breast cancer study show that the proposed classification method obtains smaller models with higher prediction accuracy than the method based on the assumption of independent observations. The second part of this thesis develops a new forecasting approach for large-scale datasets associated with a large number of predictor categories and with predictor structures. The new approach, beyond conventional tree-based methods, incorporates a general linear model and hierarchical splits to make trees more comprehensive, efficient, and interpretable. Through an empirical study in the air cargo industry and a simulation study containing several different settings, the new approach produces higher forecasting accuracy and higher computational efficiency than existing tree-based methods.
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Haensly, 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/.

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Bankruptcy is a costly event. Holders of publicly traded securities can rely on security prices to reflect their risk. Other stakeholders have no such mechanism. Hence, methods for accurately forecasting bankruptcy would be valuable to them. A large body of literature has arisen on bankruptcy forecasting with statistical classification since Beaver (1967) and Altman (1968). Reported total error rates typically are 10%-20%, suggesting that these models reveal information which otherwise is unavailable and has value after financial data is released. This conflicts with evidence on market efficiency which indicates that securities markets adjust rapidly and actually anticipate announcements of financial data. Efforts to resolve this conflict with event study methodology have run afoul of market model specification difficulties. A different approach is taken here. Most extant criticism of research design in this literature concerns inferential techniques but not sampling design. This paper attempts to resolve major sampling design issues. The most important conclusion concerns the usual choice of the individual firm as the sampling unit. While this choice is logically inconsistent with how a forecaster observes financial data over time, no evidence of bias could be found. In this paper, prediction performance is evaluated in terms of expected loss. Most authors calculate total error rates, which fail to reflect documented asymmetries in misclassification costs and prior probabilities. Expected loss overcomes this weakness and also offers a formal means to evaluate forecasts from the perspective of stakeholders other than investors. This study shows that cost of misclassifying bankruptcy must be at least an order of magnitude greater than cost of misclassifying nonbankruptcy before discriminant analysis methods have value. This conclusion follows from both sampling experiments on historical financial data and Monte Carlo experiments on simulated data. However, the Monte Carlo experiments reveal that as the cost ratio increases, robustness of linear discriminant rules improves; performance appears to depend more on the cost ratio than form of the distributions.
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Albanwan, Hessah AMYM. "Remote Sensing Image Enhancement through Spatiotemporal Filtering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.

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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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Books on the topic "Classification and spatiotemporal forecasting"

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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.

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Woo, Ming-Ko. Hydrological classification of Canadian prairie wetlands and prediction of wetland inundation in response to climatic variability. Ottawa: Canadian Wildlife Service, 1993.

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A, 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.

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Canada. Human Resources Development Canada., ed. Job futures. [Ottawa, ON: Human Resources Development Canada], 1996.

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Len'kov, Roman. Social forecasting and planning. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.

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The tutorial describes the preconditions of sociopragmatics research in Russia on the background of evolutionary processes of social prognostics of the twentieth century. Considered the essential characteristics of social forecasting, its subject and range of issues. Based on analysis of classification schemes methods of scientific forecasting offers the author's approach to classification of methods of social forecasting. Special attention is paid to the description of the characteristics, the specific application and selection procedure of the ways of making social predictions. Theoretical and applied analysis of the foundations of social design, the direction of its implementation and research methods used for it. The conceptual basis of design in education on the example of the educational process in the University. Given the model structure, rationale and testing of design solutions. The third edition of the book is dedicated to the 100th anniversary of the State University of management. Meets the current requirements of the Federal state educational standard of higher education. For students of higher educational institutions, students of humanitarian directions and specialities.
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Nguyen, 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.

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Zhuravlev, Yu I. Raspoznavanie, klassifikatsiya, prognoz: Matematicheskie metody i ikh primenie = Pattern recognition, classification, forecasting : mathematical techniques and their application, vol. 1. Moskva: Nauka, 1989.

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Labor, United States Department of. Occupational outlook handbook: 2008-2009. 2nd ed. Indianapolis: Jist Pub., 2008.

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Alig, 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.

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Alig, 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.

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Book chapters on the topic "Classification and 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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Maithili, K., S. Leelavathy, G. Karthi, and 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.

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Metcalfe, 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.

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Iezzi, Domenica Fioredistella, and 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.

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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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Kailas, Siva, Wenhao Luo, and 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.

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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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Conference papers on the topic "Classification and 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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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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Chen, Yirong, Ziyue Li, Wanli Ouyang, and 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.

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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 "Classification and 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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Cook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree, and 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.), October 2022. http://dx.doi.org/10.21079/11681/45644.

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ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set was generated using two separate map-ping methodologies: one based on in situ observations, and another based on the interpretation of satellite imagery. Existing geospatial data layers and recommendations from local rangeland experts guided site selections for both in situ and remote landform identification. A total of 18 landform types were mapped across 128 sites in New Mexico, Texas, and Mexico using the in situ (31 sites) and remote (97 sites) techniques. The final data set is critical for evaluating the ML-classification model and, ultimately, for improving dust forecasting models.
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