Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

气候变化对作物相关的疑虑构成了新的挑战，包括粮食不安全，供应稳定和经济规划。作为中央挑战之一，作物产量预测已成为机器学习领域的按压任务。尽管重要的是，预测任务是特别的复杂性，因为作物产量取决于天气，陆地，土壤质量等各种因素，以及它们的相互作用。近年来，在该域中成功应用了机器学习模型。然而，这些模型要么将他们的任务限制为相对较小的区域，或者只在单个或几年内进行研究，这使得它们难以在空间和时间上概括。在本文中，我们介绍了一种用于作物产量预测的新型图形的复发性神经网络，以纳入模型中的地理和时间知识，进一步提升预测力。我们的方法是在美国大陆的41个州的2000年历史上进行培训，验证和测试，从1981年到2019年覆盖了几年。据我们所知，这是第一种机器学习方法，可在作物产量预测中嵌入地理知识预测全国县级的作物产量。我们还通过应用众所周知的线性模型，基于树的模型，深度学习方法以及比较它们的性能来对与其他机器学习基线进行稳固的基础。实验表明，我们的提出方法始终如一地优于各种指标上现有的现有方法，验证地理空间和时间信息的有效性。

人口级社会事件，如民事骚乱和犯罪，往往对我们的日常生活产生重大影响。预测此类事件对于决策和资源分配非常重要。由于缺乏关于事件发生的真实原因和潜在机制的知识，事件预测传统上具有挑战性。近年来，由于两个主要原因，研究事件预测研究取得了重大进展：（1）机器学习和深度学习算法的开发和（2）社交媒体，新闻来源，博客，经济等公共数据的可访问性指标和其他元数据源。软件/硬件技术中的数据的爆炸性增长导致了社会事件研究中的深度学习技巧的应用。本文致力于提供社会事件预测的深层学习技术的系统和全面概述。我们专注于两个社会事件的域名：\ Texit {Civil unrest}和\ texit {犯罪}。我们首先介绍事件预测问题如何作为机器学习预测任务制定。然后，我们总结了这些问题的数据资源，传统方法和最近的深度学习模型的发展。最后，我们讨论了社会事件预测中的挑战，并提出了一些有希望的未来研究方向。

机器学习，在深入学习的进步，在过去分析时间序列方面表现出巨大的潜力。但是，在许多情况下，可以通过将其结合到学习方法中可能改善预测的附加信息。这对于由例如例如传感器位置的传感器网络而产生的数据至关重要。然后，可以通过通过图形结构建模，以及顺序（时间）信息来利用这种空间信息。适应深度学习的最新进展在各种图形相关任务中表明了有希望的潜力。但是，这些方法尚未在很大程度上适用于时间序列相关任务。具体而言，大多数尝试基本上围绕空间 - 时间图形神经网络巩固了时间序列预测的小序列长度。通常，这些架构不适合包含大数据序列的回归或分类任务。因此，在这项工作中，我们使用图形神经网络的好处提出了一种能够在多变量时间序列回归任务中处理这些长序列的架构。我们的模型在包含地震波形的两个地震数据集上进行测试，其中目标是预测在一组站的地面摇动的强度测量。我们的研究结果表明了我们的方法的有希望的结果，这是深入讨论的额外消融研究。

研究人员通常会采用数值方法来理解和预测海洋动力学，这是掌握环境现象的关键任务。在地形图很复杂，有关基础过程的知识不完整或应用程序至关重要的情况下，此类方法可能不适合。另一方面，如果观察到海洋动力学，则可以通过最近的机器学习方法来利用它们。在本文中，我们描述了一种数据驱动的方法，可以预测环境变量，例如巴西东南海岸的Santos-Sao Vicente-Bertioga estuarine系统的当前速度和海面高度。我们的模型通过连接最新的序列模型（LSTM和Transformers）以及关系模型（图神经网络）来利用时间和空间归纳偏见，以学习时间特征和空间特征，观察站点之间共享的关系。我们将结果与桑托斯运营预测系统（SOFS）进行比较。实验表明，我们的模型可以实现更好的结果，同时保持灵活性和很少的领域知识依赖性。

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.

地震阶段关联将地震到达时间测量连接到其致病来源。有效的关联必须确定离散事件的数量，其位置和起源时间，并且必须将实际到达与测量工件区分开。深度学习采摘者的出现，从紧密重叠的小地震中提供了高率的速度，它激发了重新审视相关问题并使用深度学习方法来解决它。我们已经开发了一个图形神经网络关联器，该协会同时预测源时空定位和离散的源源 - 边界关联可能性。该方法适用于任意几何形状，数百个电台的时变地震网络，并且具有可变噪声和质量的高源和输入选拔速率。我们的图形地震神经解释引擎（Genie）使用一个图来表示站点，另一个图表示空间源区域。 Genie从数据中从数据中学习了关系，使其能够确定可靠的源和源源联想。我们使用Phasenet Deep Learth Learning Phase Phase Picker生成的输入来培训合成数据，并测试来自北加州（NC）地震网络的真实数据的方法。我们成功地重新检测了USGS在2000年$ \ unicode {x2013} $ 2022之间的500天报告中报告的所有事件M> 1的96％。在2017年的100天连续处理间隔中，$ \ unicode {x2013} $ 2018，我们检测到〜4.2x USGS报告的事件数量。我们的新事件的估计值低于USGS目录的完整性幅度，并且位于该地区的活动故障和采石场附近。我们的结果表明，精灵可以在复杂的地震监测条件下有效解决关联问题。

Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.

Traffic state prediction in a transportation network is paramount for effective traffic operations and management, as well as informed user and system-level decision-making. However, long-term traffic prediction (beyond 30 minutes into the future) remains challenging in current research. In this work, we integrate the spatio-temporal dependencies in the transportation network from network modeling, together with the graph convolutional network (GCN) and graph attention network (GAT). To further tackle the dramatic computation and memory cost caused by the giant model size (i.e., number of weights) caused by multiple cascaded layers, we propose sparse training to mitigate the training cost, while preserving the prediction accuracy. It is a process of training using a fixed number of nonzero weights in each layer in each iteration. We consider the problem of long-term traffic speed forecasting for a real large-scale transportation network data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). Experimental results show that the proposed GCN-STGT and GAT-STGT models achieve low prediction errors on short-, mid- and long-term prediction horizons, of 15, 30 and 45 minutes in duration, respectively. Using our sparse training, we could train from scratch with high sparsity (e.g., up to 90%), equivalent to 10 times floating point operations per second (FLOPs) reduction on computational cost using the same epochs as dense training, and arrive at a model with very small accuracy loss compared with the original dense training

We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.

太阳能现在是历史上最便宜的电力形式。不幸的是，由于其变异性，显着提高栅格的太阳能的一部分仍然具有挑战性，这使得电力的供需平衡更加困难。虽然热发电机坡度 - 它们可以改变输出的最高速率 - 是有限的，太阳能的坡度基本上是无限的。因此，准确的近期太阳能预测或垂圈，对于提供预警来调整热发电机输出，以响应于太阳能变化来调整热发电机，以确保平衡供需。为了解决问题，本文开发了使用自我监督学习的丰富和易于使用的多光谱卫星数据的太阳能垂圈的一般模型。具体而言，我们使用卷积神经网络（CNN）和长短期内存网络（LSTM）开发深度自动回归模型，这些模型在多个位置训练全球培训，以预测最近推出的最近收集的时空数据的未来观察-R系列卫星。我们的模型估计了基于卫星观测的未来的太阳辐照度，我们向较小的场地特定的太阳能数据培训的回归模型提供，以提供近期太阳能光伏（PV）预测，其考虑了现场特征的特征。我们评估了我们在25个太阳能场所的不同覆盖区域和预测视野的方法，并表明我们的方法利用地面真理观察结果产生靠近模型的错误。

图表神经网络（GNNS）最近在人工智能（AI）领域的普及，这是由于它们作为输入数据相对非结构化数据类型的独特能力。尽管GNN架构的一些元素在概念上类似于传统神经网络（以及神经网络变体）的操作中，但是其他元件代表了传统深度学习技术的偏离。本教程通过整理和呈现有关GNN最常见和性能变种的动机，概念，数学和应用的细节，将GNN的权力和新颖性暴露给AI从业者。重要的是，我们简明扼要地向实际示例提出了本教程，从而为GNN的主题提供了实用和可访问的教程。

对于电网操作，具有精细时间和空间分辨率的太阳能发电准确预测对于电网的操作至关重要。然而，与数值天气预报（NWP）结合机器学习的最先进方法具有粗略分辨率。在本文中，我们采用曲线图信号处理透视和型号的多网站光伏（PV）生产时间序列作为图表上的信号，以捕获它们的时空依赖性并实现更高的空间和时间分辨率预测。我们提出了两种新颖的图形神经网络模型，用于确定性多站点PV预测，被称为图形 - 卷积的长期内存（GCLSTM）和图形 - 卷积变压器（GCTRAFO）模型。这些方法仅依赖于生产数据并利用PV系统提供密集的虚拟气象站网络的直觉。所提出的方法是在整整一年的两组数据集中评估：1）来自304个真实光伏系统的生产数据，以及2）模拟生产1000个PV系统，包括瑞士分布。该拟议的模型优于最先进的多站点预测方法，用于预测前方6小时的预测视野。此外，所提出的模型以NWP优于最先进的单站点方法，如前方的视野上的输入。

Pre-publication draft of a book to be published byMorgan & Claypool publishers. Unedited version released with permission. All relevant copyrights held by the author and publisher extend to this pre-publication draft.

COVID-19的大流行提出了对多个领域决策者的流行预测的重要性，从公共卫生到整个经济。虽然预测流行进展经常被概念化为类似于天气预测，但是它具有一些关键的差异，并且仍然是一项非平凡的任务。疾病的传播受到人类行为，病原体动态，天气和环境条件的多种混杂因素的影响。由于政府公共卫生和资助机构的倡议，捕获以前无法观察到的方面的丰富数据来源的可用性增加了研究的兴趣。这尤其是在“以数据为中心”的解决方案上进行的一系列工作，这些解决方案通过利用非传统数据源以及AI和机器学习的最新创新来增强我们的预测能力的潜力。这项调查研究了各种数据驱动的方法论和实践进步，并介绍了一个概念框架来导航它们。首先，我们列举了与流行病预测相关的大量流行病学数据集和新的数据流，捕获了各种因素，例如有症状的在线调查，零售和商业，流动性，基因组学数据等。接下来，我们将讨论关注最近基于数据驱动的统计和深度学习方法的方法和建模范式，以及将机械模型知识域知识与统计方法的有效性和灵活性相结合的新型混合模型类别。我们还讨论了这些预测系统的现实部署中出现的经验和挑战，包括预测信息。最后，我们重点介绍了整个预测管道中发现的一些挑战和开放问题。

As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services. UberNet empploys a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. The proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet's prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.

在许多研究中已经表明，考虑相关股票数据预测股票价格变动的重要性，但是，用于建模，嵌入和分析相互关联股票行为的先进图形技术尚未被广泛利用，以预测股票价格变动。该领域的主要挑战是找到一种建模任意股票之间现有关系的方法，并利用这种模型来改善这些股票的预测绩效。该领域中的大多数现有方法都取决于基本的图形分析技术，预测能力有限，并且缺乏通用性和灵活性。在本文中，我们介绍了一个名为GCNET的新颖框架，该框架将任意股票之间的关系建模为称为“影响网络”的图形结构，并使用一组基于历史的预测模型来推断出股票子集的合理初始标签图中的节点。最后，GCNET使用图形卷积网络算法来分析此部分标记的图形，并预测图中每个库存的下一个运动价格方向。 GCNET是一个一般预测框架，可以根据其历史数据来预测相互作用股票的价格波动。我们对纳斯达克指数一组股票的实验和评估表明，GCNET在准确性和MCC测量方面显着提高了SOTA的性能。

Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field's youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

近年来，图形神经网络（GNN）与复发性神经网络（RNN）的变体相结合，在时空预测任务中达到了最先进的性能。对于流量预测，GNN模型使用道路网络的图形结构来解释链接和节点之间的空间相关性。最近的解决方案要么基于复杂的图形操作或避免预定义的图。本文提出了一种新的序列结构，以使用具有稀疏体系结构的GNN-RNN细胞在多个抽象的抽象上提取时空相关性，以减少训练时间与更复杂的设计相比。通过多个编码器编码相同的输入序列，并随着编码层的增量增加，使网络能够通过多级抽象来学习一般和详细的信息。我们进一步介绍了来自加拿大蒙特利尔的街道细分市场流量数据的新基准数据集。与高速公路不同，城市路段是循环的，其特征是复杂的空间依赖性。与基线方法相比，一小时预测的实验结果和我们的MSLTD街道级段数据集对我们的模型提高了7％以上，同时将计算资源要求提高了一半以上竞争方法。