衡量全球经济均衡的定量指标与农业供应链和国际贸易流量具有强大而相互依存的关系。这些过程中的突然震动由贸易战争，流行病或天气等异常事件造成的，可能对全球经济具有复杂影响。在本文中，我们提出了一种新颖的框架，即：Depeag，采用经济学，使用深度学习（DL）来测量异常事件检测的影响，以确定普通财务指数（如Dowjones）之间的关系，以及生产价值农产品（如奶酪和牛奶）。我们使用称为长期内存（LSTM）网络的DL技术成功地预测商品生产，高精度，也是五个流行的模型（回归和提升）作为基准，以测量异常事件的影响。结果表明，具有异常值的考虑因素（使用隔离林）优于基线模型的Depeag，以及具有异常值检测的相同模型。在预测财务指标预测商品生产时，异常事件会产生相当大的影响。此外，我们展示了Deepag对公共政策的影响，为政策制定者和农民提供了洞察力，以及农业生态系统的运作决策。收集数据，模型开发，并记录和呈现结果。

国际经济学具有改善对造成贸易的因素的理解历史，以及在各国的自由货物和服务的后果。最近对自由贸易制度的冲击，特别是主要经济体之间的贸易纠纷，以及贸易战争和大流行等黑色天鹅赛事，提高了改进预测，以告知政策决定。 AI方法允许经济学家以新的方式解决这些预测问题。在这一稿件中，我们提出了新的方法，预测和助会在国际上交易的食物和农产品。关联规则（AR）分析已成功部署消费者或商店水平的经济场景，例如市场篮子分析。然而，在我们的工作中，我们对进口和出口协会的分析及其对商品贸易流量的影响。此外，开发了集成机器学习方法，以提供改善的农业贸易预测，异常事件的影响，以及对政策制定者的定量指针。

Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, four different demand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator) and LSTM (Long Short-Term Memory) networks are utilized and evaluated. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and a machine learning technique for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariate approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach such as ARIMA appears to be sufficient. We also comment on the approach to choose clinical indicators (inputs) for the multivariate models.

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.

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.

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.

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.

股票市场是一个网络，为几乎所有主要的经济交易提供平台。虽然投资股票市场是一个好主意，但对单个股票进行投资可能不是一个好主意，尤其是对于休闲投资者而言。智能储备需要深入研究和大量奉献精神。预测这种股票价值提供了巨大的套利利润机会。找到解决方案的这种吸引力促使研究人员找到了过去的问题，例如波动，季节性和时间依赖时间。本文调查了自然语言处理和机器学习技术领域的最新文献，用于预测股票市场的发展。本文的主要贡献包括许多最近的文章的复杂分类以及股票市场预测研究及其相关领域的最新研究趋势。

电力是一种波动的电源，需要短期和长期的精力计划和资源管理。更具体地说，在短期，准确的即时能源消耗中，预测极大地提高了建筑物的效率，为采用可再生能源提供了新的途径。在这方面，数据驱动的方法，即基于机器学习的方法，开始优先于更传统的方法，因为它们不仅提供了更简化的部署方式，而且还提供了最新的结果。从这个意义上讲，这项工作应用和比较了几种深度学习算法，LSTM，CNN，CNN-LSTM和TCN的性能，在制造业内的一个真实测试中。实验结果表明，TCN是预测短期即时能源消耗的最可靠方法。

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.

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。

异常值是一个事件或观察，其被定义为不同于距群体的不规则距离的异常活动，入侵或可疑数据点。然而，异常事件的定义是主观的，取决于应用程序和域（能量，健康，无线网络等）。重要的是要尽可能仔细地检测异常事件，以避免基础设施故障，因为异常事件可能导致对基础设施的严重损坏。例如，诸如微电网的网络物理系统的攻击可以发起电压或频率不稳定性，从而损坏涉及非常昂贵的修复的智能逆变器。微电网中的不寻常活动可以是机械故障，行为在系统中发生变化，人体或仪器错误或恶意攻击。因此，由于其可变性，异常值检测（OD）是一个不断增长的研究领域。在本章中，我们讨论了使用AI技术的OD方法的进展。为此，通过多个类别引入每个OD模型的基本概念。广泛的OD方法分为六大类：基于统计，基于距离，基于密度的，基于群集的，基于学习的和合奏方法。对于每个类别，我们讨论最近最先进的方法，他们的应用领域和表演。之后，关于对未来研究方向的建议提供了关于各种技术的优缺点和挑战的简要讨论。该调查旨在指导读者更好地了解OD方法的最新进展，以便保证AI。

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.

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

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.

对于长期来说，研究人员一直在开发可靠而准确的股票价格预测预测模型。根据文献，如果预测模型是正确的设计和精炼，他们可以煞费苦心地和忠实地估计未来的库存价值。本文展示了一组时间序列，计量经济性和各种基于学习的股票价格预测模型。在此处使用来自2004年1月至2019年12月至2019年12月的Infosys，Icici和Sun Pharma的数据用于培训和测试模型，以了解哪种模型在哪个部门中表现最佳。一个时间序列模型（Holt-Winters指数平滑），一个计量计量模型（Arima），两台机器学习模型（随机林和火星），以及两种深度学习的模型（简单的RNN和LSTM）已被列入本文。火星已被证明是最好的执行机器学习模式，而LSTM已被证明是表现最好的深层学习模式。但总体而言，对于所有三个部门 - 它（在Infosys数据上），银行业务（在ICICI数据）和健康（在Sun Pharma数据上），Mars已被证明是销售预测中最佳表现模式。

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。

近年来，随着传感器和智能设备的广泛传播，物联网（IoT）系统的数据生成速度已大大增加。在物联网系统中，必须经常处理，转换和分析大量数据，以实现各种物联网服务和功能。机器学习（ML）方法已显示出其物联网数据分析的能力。但是，将ML模型应用于物联网数据分析任务仍然面临许多困难和挑战，特别是有效的模型选择，设计/调整和更新，这给经验丰富的数据科学家带来了巨大的需求。此外，物联网数据的动态性质可能引入概念漂移问题，从而导致模型性能降解。为了减少人类的努力，自动化机器学习（AUTOML）已成为一个流行的领域，旨在自动选择，构建，调整和更新机器学习模型，以在指定任务上实现最佳性能。在本文中，我们对Automl区域中模型选择，调整和更新过程中的现有方法进行了审查，以识别和总结将ML算法应用于IoT数据分析的每个步骤的最佳解决方案。为了证明我们的发现并帮助工业用户和研究人员更好地实施汽车方法，在这项工作中提出了将汽车应用于IoT异常检测问题的案例研究。最后，我们讨论并分类了该领域的挑战和研究方向。

良好的研究努力致力于利用股票预测中的深度神经网络。虽然远程依赖性和混沌属性仍然是在预测未来价格趋势之前降低最先进的深度学习模型的表现。在这项研究中，我们提出了一个新的框架来解决这两个问题。具体地，在将时间序列转换为复杂网络方面，我们将市场价格系列转换为图形。然后，从映射的图表中提取参考时间点和节点权重之间的关联的结构信息以解决关于远程依赖性和混沌属性的问题。我们采取图形嵌入式以表示时间点之间的关联作为预测模型输入。节点重量被用作先验知识，以增强时间关注的学习。我们拟议的框架的有效性通过现实世界股票数据验证，我们的方法在几个最先进的基准中获得了最佳性能。此外，在进行的交易模拟中，我们的框架进一步获得了最高的累积利润。我们的结果补充了复杂网络方法在金融领域的现有应用，并为金融市场中决策支持的投资应用提供了富有识别的影响。