一些现实世界决策问题需要立即对多个步骤进行概率预测。然而,概率预测方法可能无法捕获在长时间视野中存在的基础时间序列中的相关性,因为累积累积。一个这样的应用是在网格环境中不确定性下的资源调度,这需要预测电力需求,这是自然嘈杂的,但通常是循环的。在本文中,我们介绍了条件近似标准化流量(CANF),以便在长时间视野中存在相关性时进行概率的多步时间序列预测。我们首先展示了我们对估计玩具分布密度的方法的功效,发现CANF与高斯混合模型相比通过三分之一提高了KL发散,同时仍可用于显式调理。然后,我们使用公开的家用电力消耗数据集来展示CANF在联合概率多步预测上的有效性。经验结果表明,条件近似标准化流动在多步骤预测精度方面优于其他方法,并导致高达10倍的调度决策。我们的实现可在https://github.com/sisl/jointdemandforecast中获得。
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基于预测方法的深度学习已成为时间序列预测或预测的许多应用中的首选方法,通常通常优于其他方法。因此,在过去的几年中,这些方法现在在大规模的工业预测应用中无处不在,并且一直在预测竞赛(例如M4和M5)中排名最佳。这种实践上的成功进一步提高了学术兴趣,以理解和改善深厚的预测方法。在本文中,我们提供了该领域的介绍和概述:我们为深入预测的重要构建块提出了一定深度的深入预测;随后,我们使用这些构建块,调查了最近的深度预测文献的广度。
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电力行业正在大力实施智能网格技术,以提高可靠性,可用性,安全性和效率。该实施需要技术进步,标准和法规的发展以及测试和计划。智能电网载荷预测和管理对于降低需求波动和改善连接发电机,分销商和零售商的市场机制至关重要。在政策实施或外部干预措施中,有必要分析其对电力需求的影响的不确定性,以使系统对需求的波动更加准确。本文分析了外部干预的不确定性对电力需求的影响。它实现了一种结合概率和全局预测模型的框架,使用深度学习方法来估计干预措施的因果影响分布。通过预测受影响实例的反事实分布结果,然后将其与实际结果进行对比来评估因果效应。我们将COVID-19锁定对能源使用的影响视为评估这种干预对电力需求分布的不均匀影响的案例研究。我们可以证明,在澳大利亚和某些欧洲国家的最初封锁期间,槽通常比峰值更大的下降,而平均值几乎不受影响。
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We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting. Prior approaches are either autoregressive, implicitly capturing the dependency structure across time but exhibiting error accumulation with increasing forecast horizons, or multi-horizon sequence-to-sequence models, which do not exhibit error accumulation, but also do typically not model the dependency structure across time steps. MQF$^2$ combines the benefits of both approaches, by directly making predictions in the form of a multivariate quantile function, defined as the gradient of a convex function which we parametrize using input-convex neural networks. By design, the quantile function is monotone with respect to the input quantile levels and hence avoids quantile crossing. We provide two options to train MQF$^2$: with energy score or with maximum likelihood. Experimental results on real-world and synthetic datasets show that our model has comparable performance with state-of-the-art methods in terms of single time step metrics while capturing the time dependency structure.
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Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.
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电力在不同的时间范围和法规上在各个市场上进行交易。由于更高的可再生能源渗透,短期交易变得越来越重要。在德国,盘中电价通常以独特的小时模式围绕EPEX现货市场的白天价格波动。这项工作提出了一种概率建模方法,该方法对日前合同的盘中价格差异进行了建模。该模型通过将每天的每日价格间隔的四个15分钟的间隔视为四维的关节分布,从而捕获了新兴的小时模式。使用归一化流量,即结合条件多元密度估计和概率回归的深层生成模型,从而学习了最终的多元价格差异分布。将归一化流程与选择的历史数据,高斯副群和高斯回归模型进行了比较。在不同的模型中,归一化流量最准确地识别趋势,并且预测间隔最窄。值得注意的是,归一化流是唯一识别稀有价格峰的方法。最后,这项工作讨论了不同外部影响因素的影响,并发现个人大多数因素都可以忽略不计。只有价格差异实现的直接历史和所有投入因素的组合才能显着改善预测。
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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.
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Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto-regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 15% compared to state-of-the-art methods.
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随机微分方程的系统定义了一系列随机波动率模型。尽管这些模型在金融和统计气候学等领域中取得了广泛的成功,但它们通常缺乏在历史数据上条件产生真正的后验分布的能力。为了解决这一基本限制,我们展示了如何将一类随机波动率模型重新塑造为具有专门协方差函数的层次高斯工艺(GP)模型。该GP模型保留了随机波动率模型的电感偏差,同时提供了GP推断给出的后验预测分布。在此框架内,我们从研究良好的域中汲取灵感,以引入新的型号,即Volt和Magpie,这些模型在库存和风速预测中的表现明显超过了基线,并且自然扩展到多任务设置。
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With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.
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机器学习的许多应用涉及预测模型输出的灵活概率分布。我们提出了自动评级分位式流动,这是一种灵活的概率模型,高维变量,可用于准确地捕获预测的炼膜不确定性。这些模型是根据适当评分规则使用新颖目标培训的自回归流动的情况,这简化了培训期间雅各比亚的计算昂贵的决定因素,并支持新型的神经结构。我们证明这些模型可用于参数化预测条件分布,提高时间序列预测和对象检测的概率预测质量。
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时间变化数量的估计是医疗保健和金融等领域决策的基本组成部分。但是,此类估计值的实际实用性受到它们量化预测不确定性的准确程度的限制。在这项工作中,我们解决了估计高维多元时间序列的联合预测分布的问题。我们提出了一种基于变压器体系结构的多功能方法,该方法使用基于注意力的解码器估算关节分布,该解码器可被学会模仿非参数Copulas的性质。最终的模型具有多种理想的属性:它可以扩展到数百个时间序列,支持预测和插值,可以处理不规则和不均匀的采样数据,并且可以在训练过程中无缝地适应丢失的数据。我们从经验上证明了这些属性,并表明我们的模型在多个现实世界数据集上产生了最新的预测。
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在本文中,我们介绍了蒙面的多步多变量预测(MMMF),这是一个新颖而普遍的自我监督学习框架,用于时间序列预测,并提供已知的未来信息。在许多真实世界的预测情况下,已知一些未来的信息,例如,在进行短期到中期的电力需求预测或进行飞机出发预测时的油价预测时,天气信息。现有的机器学习预测框架可以分为(1)基于样本的方法,在此方法中进行每个预测,以及(2)时间序列回归方法,其中未来信息未完全合并。为了克服现有方法的局限性,我们提出了MMMF,这是一个培训能够生成一系列输出的神经网络模型的框架,将过去的时间信息和有关未来的已知信息结合在一起,以做出更好的预测。实验在两个现实世界数据集上进行(1)中期电力需求预测,以及(2)前两个月的飞行偏离预测。他们表明,所提出的MMMF框架的表现不仅优于基于样本的方法,而且具有与完全相同的基本模型的现有时间序列预测模型。此外,一旦通过MMMF进行了神经网络模型,其推理速度与接受传统回归配方训练的相同模型的推理速度相似,从而使MMMF成为现有回归训练的时间序列的更好替代品,如果有一些可用的未来,信息。
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A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.
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评估能源转型和能源市场自由化对资源充足性的影响是一种越来越重要和苛刻的任务。能量系统的上升复杂性需要足够的能量系统建模方法,从而提高计算要求。此外,随着复杂性,同样调用概率评估和场景分析同样增加不确定性。为了充分和高效地解决这些各种要求,需要来自数据科学领域的新方法来加速当前方法。通过我们的系统文献综述,我们希望缩小三个学科之间的差距(1)电力供应安全性评估,(2)人工智能和(3)实验设计。为此,我们对所选应用领域进行大规模的定量审查,并制作彼此不同学科的合成。在其他发现之外,我们使用基于AI的方法和应用程序的AI方法和应用来确定电力供应模型的复杂安全性的元素,并作为未充分涵盖的应用领域的储存调度和(非)可用性。我们结束了推出了一种新的方法管道,以便在评估电力供应安全评估时充分有效地解决当前和即将到来的挑战。
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最近实现了更准确的短期预测的数据驱动的空气质量预测。尽管取得了成功,但大多数目前的数据驱动解决方案都缺乏适当的模型不确定性的量化,以传达信任预测的程度。最近,在概率深度学习中已经制定了几种估计不确定性的实用工具。但是,在空气质量预测领域的域中没有经验应用和广泛的比较这些工具。因此,这项工作在空气质量预测的真实环境中应用了最先进的不确定性量化。通过广泛的实验,我们描述了培训概率模型,并根据经验性能,信心可靠性,置信度估计和实际适用性评估其预测性不确定性。我们还使用空气质量数据中固有的“自由”对抗培训和利用时间和空间相关性提出改善这些模型。我们的实验表明,所提出的模型比以前的工作更好地在量化数据驱动空气质量预测中的不确定性方面表现出。总体而言,贝叶斯神经网络提供了更可靠的不确定性估计,但可能挑战实施和规模。其他可扩展方法,如深合奏,蒙特卡罗(MC)辍学和随机重量平均-Gaussian(SWAG)可以执行良好,如果正确应用,但具有不同的权衡和性能度量的轻微变化。最后,我们的结果表明了不确定性估计的实际影响,并证明了,实际上,概率模型更适合提出知情决策。代码和数据集可用于\ url {https:/github.com/abdulmajid-murad/deep_probabilistic_forecast}
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低压网络中分布式能源的渗透不断增加,这将最终用户从消费者转变为生产者。但是,由于零售和网络服务提供的监管分离,智能电表数据的不完整智能电表的推出和缺乏智能电表数据,这使主动分配网络管理变得困难。此外,分销网络运营商通常无法访问实时智能电表数据,这会带来额外的挑战。由于缺乏更好的解决方案,他们使用毯子屋顶太阳能出口限制,从而导致次优结果。为了解决这个问题,我们设计了一个有条件的生成对抗网络(CGAN)的模型来预测家庭太阳能产生和电力需求,这是用于在不确定性下用于计算公平操作信封的机会约束最佳功率流的输入。
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Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.
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预测组合在预测社区中蓬勃发展,近年来,已经成为预测研究和活动主流的一部分。现在,由单个(目标)系列产生的多个预测组合通过整合来自不同来源收集的信息,从而提高准确性,从而减轻了识别单个“最佳”预测的风险。组合方案已从没有估计的简单组合方法演变为涉及时间变化的权重,非线性组合,组件之间的相关性和交叉学习的复杂方法。它们包括结合点预测和结合概率预测。本文提供了有关预测组合的广泛文献的最新评论,并参考可用的开源软件实施。我们讨论了各种方法的潜在和局限性,并突出了这些思想如何随着时间的推移而发展。还调查了有关预测组合实用性的一些重要问题。最后,我们以当前的研究差距和未来研究的潜在见解得出结论。
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在称为RNN(p)的几个时间滞后的复发神经网络是自然回归ARX(P)模型的自然概括。当不同的时间尺度会影响给定现象时,它是一种强大的预测工具,因为它发生在能源领域,每小时,每日,每周和每年的互动并存。具有成本效益的BPTT是RNN的学习算法的行业标准。我们证明,当训练RNN(P)模型时,其他学习算法在时间和空间复杂性方面都更加有效。我们还介绍了一种新的学习算法,即树木重组的重组学习,该算法利用了展开网络的树表示,并且似乎更有效。我们提出了RNN(P)模型的应用,以在每小时规模上进行功耗预测:实验结果证明了所提出的算法的效率以及所选模型在点和能源消耗的概率预测中实现的出色预测准确性。
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