In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.

短期负荷预测（STLF）由于复杂的时间序列（TS）是一种表达三个季节性模式和非线性趋势的挑战。本文提出了一种新的混合分层深度学习模型，涉及多个季节性，并产生两点预测和预测间隔（PIS）。它结合了指数平滑（ES）和经常性神经网络（RNN）。 ES动态提取每个单独的TS的主要组件，并启用在飞行的临时化，这在相对较小的数据集上操作时特别有用。多层RNN配备了一种新型扩张的经常性电池，旨在有效地模拟TS中的短期和长期依赖性。为了改善内部TS表示，因此模型的性能，RNN同时学习ES参数和主要映射函数将输入转换为预测。我们比较我们对几种基线方法的方法，包括古典统计方法和机器学习（ML）方法，在35个欧洲国家的STLF问题。实证研究清楚地表明，该模型具有高表现力，以解决非线性随机预测问题，包括多个季节性和显着的随机波动。实际上，它在准确性方面优于统计和最先进的ML模型。

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.

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。

Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract spatial and temporal information from multi-dimensional input data to make day-ahead predictions. In this regard, our method incorporates an ultra-wide learning view, combining data from multiple numerical weather prediction models, wind farms, and geographical locations. Additionally, we experiment with global forecasting approaches to understand the impact of training the same model over the datasets obtained from multiple different wind farms, and we employ a method where spatial information extracted from convolutional layers is passed to a tree ensemble (e.g., Light Gradient Boosting Machine (LGBM)) instead of fully connected layers. The results show that our proposed CNN-RNN architecture outperforms other models such as LGBM, Extra Tree regressor and linear regression when trained globally, but fails to replicate such performance when trained individually on each farm. We also observe that passing the spatial information from CNN to LGBM improves its performance, providing further evidence of CNN's spatial feature extraction capabilities.

杂交和集合学习技术是改善预测方法的预测能力的流行模型融合技术。通过有限的研究，将这两种有前途的方法结合在一起，本文着重于不同合奏的基础模型池中指数平滑的旋转神经网络（ES-RNN）的实用性。我们将某些最先进的结合技术和算术模型平均作为基准进行比较。我们对M4预测数据集进行了100,000个时间序列，结果表明，基于特征的预测模型平均（FFORFORA）平均是与ES-RNN的晚期数据融合的最佳技术。但是，考虑到M4的每日数据子集，堆叠是处理所有基本模型性能相似的情况下唯一成功的合奏。我们的实验结果表明，与N-Beats作为基准相比，我们达到了艺术的预测结果。我们得出的结论是，模型平均比模型选择和堆叠策略更强大。此外，结果表明，提高梯度对于实施合奏学习策略是优越的。

预测基金绩效对投资者和基金经理都是有益的，但这是一项艰巨的任务。在本文中，我们测试了深度学习模型是否比传统统计技术更准确地预测基金绩效。基金绩效通常通过Sharpe比率进行评估，该比例代表了风险调整的绩效，以确保基金之间有意义的可比性。我们根据每月收益率数据序列数据计算了年度夏普比率，该数据的时间序列数据为600多个投资于美国上市大型股票的开放式共同基金投资。我们发现，经过现代贝叶斯优化训练的长期短期记忆（LSTM）和封闭式复发单元（GRUS）深度学习方法比传统统计量相比，预测基金的Sharpe比率更高。结合了LSTM和GRU的预测的合奏方法，可以实现所有模型的最佳性能。有证据表明，深度学习和结合能提供有希望的解决方案，以应对基金绩效预测的挑战。

随着高级数字技术的蓬勃发展，用户以及能源分销商有可能获得有关家庭用电的详细信息。这些技术也可以用来预测家庭用电量（又称负载）。在本文中，我们研究了变分模式分解和深度学习技术的使用，以提高负载预测问题的准确性。尽管在文献中已经研究了这个问题，但选择适当的分解水平和提供更好预测性能的深度学习技术的关注较少。这项研究通过研究六个分解水平和五个不同的深度学习网络的影响来弥合这一差距。首先，使用变分模式分解将原始负载轮廓分解为固有模式函数，以减轻其非平稳方面。然后，白天，小时和过去的电力消耗数据作为三维输入序列馈送到四级小波分解网络模型。最后，将与不同固有模式函数相关的预测序列组合在一起以形成聚合预测序列。使用摩洛哥建筑物的电力消耗数据集（MORED）的五个摩洛哥家庭的负载曲线评估了该方法，并根据最新的时间序列模型和基线持久性模型进行了基准测试。

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.

我们在在线环境中研究了非线性预测，并引入了混合模型，该模型通过端到端体系结构有效地减轻了对手工设计的功能的需求和传统非线性预测/回归方法的手动模型选择问题。特别是，我们使用递归结构从顺序信号中提取特征，同时保留状态信息，即历史记录和增强决策树以产生最终输出。该连接是以端到端方式的，我们使用随机梯度下降共同优化整个体系结构，我们还为此提供了向后的通过更新方程。特别是，我们采用了一个经常性的神经网络（LSTM）来从顺序数据中提取自适应特征，并提取梯度增强机械（Soft GBDT），以进行有效的监督回归。我们的框架是通用的，因此可以使用其他深度学习体系结构进行特征提取（例如RNN和GRU）和机器学习算法进行决策，只要它们是可区分的。我们证明了算法对合成数据的学习行为以及各种现实生活数据集对常规方法的显着性能改进。此外，我们公开分享提出的方法的源代码，以促进进一步的研究。

已经显示混合方法以在预测任务中以纯粹的统计和纯粹的深度学习方法优于预测，并定量与这些预测（预测间隔）的相关不确定性。一个示例是指数平滑复发性神经网络（ES-RNN），统计预测模型和经常性神经网络变体之间的混合。 ES-RNN在Makridakis-4预测竞争中实现了9.4 \％的绝对错误。这种改进和类似的混合模型的表现主要是仅在单变量数据集上展示。将混合预测方法应用于多变量数据的困难包括（$ i $）的高参数调整所涉及的高计算成本，用于与数据中固有的自动关联相关的模型（II $）挑战，以及（ $ iii $）在可能难以捕获的协变量之间的复杂依赖（交叉相关）。本文介绍了多变量指数平滑的长短短期记忆（MES-LSTM），对ES-RNN的广义多元扩展，克服了这些挑战。 MES-LSTM利用了矢量化实现。我们在2019年（Covid-19）发病率数据集的几种聚集冠状病毒病中测试MES-LSTM，并发现我们的混合方法在预测准确性和预测间隔建设下对纯统计和深度学习方法进行了一致的，显着改善。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

电力公用事业公司依靠短期需求预测，以期待重大变化的预期调整生产和分配。该系统审查分析了2000年至2019年之间的学术期刊上发布的240份作品，专注于将人工智能（AI），统计和混合模型应用于短期负荷预测（STLF）。这项工作代表了迄今为止对该主题的最全面的审查。进行了对文献的完整分析，以确定最流行和最准确的技术以及现有的空隙。研究结果表明，尽管人工神经网络（ANN）继续成为最常用的独立技术，但研究人员已经超出了不同技术的混合组合，以利用各种方法的组合优势。审查表明，这些混合组合通常可以实现超过99％的预测精度。短期预测最成功的持续时间已被识别为每小时间隔的一天的预测。审查已确定访问培训模型所需的数据集的不足。在亚洲，欧洲，北美和澳大利亚以外的研究区域中已经确定了一个显着差距。

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.

在这项工作中，我们提出了使用量子缩放（MQ-DRN-S）的分位数回归和扩张的经常性神经网络，并将其应用于库存管理任务。该模型在统计基准（具有外源性变量，QAR-X）的统计基准（分位式自回归模型，QAR-X）而言，该模型更好地表现出更好的性能，而不是在没有时间缩放的MQ-DRNN的情况下更好。以上一系列10,000次销售的elllobo销售超过53周的地平线，每周使用滚动窗口为7天。

在本文中，我们呈现SSDNet，这是一个新的时间序列预测的深层学习方法。SSDNet将变压器架构与状态空间模型相结合，提供概率和可解释的预测，包括趋势和季节性成分以及前一步对预测很重要。变压器架构用于学习时间模式并直接有效地估计状态空间模型的参数，而无需对卡尔曼滤波器的需要。我们全面评估了SSDNET在五个数据集上的性能，显示SSDNet是一种有效的方法，可在准确性和速度，优于最先进的深度学习和统计方法方面是一种有效的方法，能够提供有意义的趋势和季节性组件。

本文介绍了一个集成预测方法，通过减少特征和模型选择假设来显示M4Competitiation数据集的强劲结果，称为甜甜圈（不利用人为假设）。我们的假设减少，主要由自动生成的功能和更多样化的集合模型组成，显着优于Montero-Manso等人的统计特征的集合方法FForma。 （2020）。此外，我们用长短期内存网络（LSTM）AutoEncoder调查特征提取，并发现此类特征包含传统统计特征方法未捕获的重要信息。合奏加权模型使用LSTM功能和统计功能准确地结合模型。特征重要性和交互的分析表明，单独的统计数据的LSTM特征略有优势。聚类分析表明，不同的基本LSTM功能与大多数统计特征不同。我们还发现，通过使用新模型增强合奏来增加加权模型的解决方案空间是加权模型学习使用的东西，解释了准确性的一部分。最后，我们为集合的最佳组合和选择提供了正式的前后事实分析，通过M4数据集的线性优化量化差异。我们还包括一个简短的证据，模型组合优于模型选择，后者。

信息爆炸的时代促使累积巨大的时间序列数据，包括静止和非静止时间序列数据。最先进的算法在处理静止时间数据方面取得了体面的性能。然而，解决静止​​时间系列的传统算法不适用于外汇交易的非静止系列。本文调查了适用的模型，可以提高预测未来非静止时间序列序列趋势的准确性。特别是，我们专注于识别潜在模型，并调查识别模式从历史数据的影响。我们提出了基于RNN的\ Rebuttal {The} SEQ2Seq模型的组合，以及通过动态时间翘曲和Zigzag峰谷指示器提取的注重机制和富集的集合特征。定制损失函数和评估指标旨在更加关注预测序列的峰值和谷点。我们的研究结果表明，我们的模型可以在外汇数据集中预测高精度的4小时未来趋势，这在逼真的情况下至关重要，以协助外汇交易决策。我们进一步提供了对各种损失函数，评估指标，模型变体和组件对模型性能的影响的评估。

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.

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.