Forecasting of electricity price through a functional prediction of sale and purchase curves

This work proposes a new approach for the prediction of the electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model the hourly purchase and the sale curves, to predict them and to find the intersection of the predicted curves in order to obtain the predicted equilibrium market price and volume. Modeling and forecasting of purchase and sale curves is performed by means of functional data analysis methods. More specifically, parametric (FAR) and nonparametric (NPFAR) functional autoregressive models are considered and compared to some benchmarks. An appealing feature of the functional approach is that, unlike other methods, it provides insights into the sale and purchase mechanism connected with the price and demand formation process and can therefore be used for the optimization of bidding strategies. An application to the Italian electricity market (IPEX) is also provided, showing that NPFAR models lead to a statistically significant improvement in the forecasting accuracy.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.     

基于频率的Read Mapping种子选择算法

选择具有最低频率的最优种子是一个复杂的计算问题,往往需要很长时间.提出了一种read的基于频率的合并种子选择算法(FMSS),该算法能够高效地选择接近最优的种子集合,可用于改善现有映射工具的性能.实验对比了平均种子选择方法和当前最优的种子选择策略(OSS,optimal seed solver),结果显示FMSS算法能够用很少的时间代价给出接近OSS的最优种子集合,这表明FMSS算法可集成到现有映射工具中用于处理更大规模的read mapping问题.     

物联网和智慧城市数据处理与管理概念体系构建——以ITU-T数据处理与管理焦点组标准化项目为例

概念体系构建和术语工作是制定任何标准的基础,在多学科和多领域的场景中,构建概念体系和术语工作面临不同利益相关方需求不同而难以达成共识的巨大挑战。文章梳理了ITU-T FG-DPM在促进不同利益相关方和项目组成员之间达成通用概念共识构建统一术语及定义的经验,通过规范概念体系的构建过程,采用术语多维度协同视角来构建统一的数据处理与管理概念体系,促进了工作组和项目组内外在物联网与智慧城市领域数据处理与管理方面达成共识。文章对多学科多领域场景的概念体系构建和术语工作具有一定参考意义,并不局限于物联网和智慧城市领域。     

Curvelet域GPR数据噪声压制与高频补偿方法

提出GPR数据Curvelet域随机噪声压制与高频补偿同步处理方法.首先，将GPR数据变换到Curvelet域，结合其多角度、多尺度的稀疏性，给出随尺度和角度变化的自适应阈值函数进行随机噪声的压制;其次，根据电磁波在完全弹性介质中的传播规律，结合Curvelet的多尺度多角度特性，求取时变补偿因子，倒数加权对应的尺度、角度，补偿高频损失;最后，进行Curvelet反变换，获得随机噪声压制与高频补偿以后的GPR数据.该方法属于完全数据驱动，克服了传统方法人为因素的影响.     

基于广义梯形模糊数相似度的交叉效率集结

在不确定环境下,针对模糊数据的多样性和复杂性,本文结合广义梯形模糊数相似度理论将广义梯形模糊数的交叉效率矩阵转换为交叉互评相似度矩阵,并根据模糊信息检索系统中的布尔模型,以能最大程度还原信息本身为准则,构建满足"正相容性"的有序几何平均集结函数对交叉互评相似度矩阵进行集结,从而得到关于每个决策单元的同行评价综合相似度.根据决策单元的同行评价综合相似度,建立反映决策者偏好的模糊一致性偏好矩阵,并对决策单元进行集结权重的分配,根据分配结果计算决策单元的全局交叉效率值.本文的集结方法可以解决不确定环境下,交叉效率矩阵数据多样性的问题,有较高的适用性;并且其集结结果具有较高的一致性和稳定性.最后本文以梯形模糊数的交叉效率矩阵为算例,来说明该方法的合理性和适用性.     

大数据营销:综述与展望

随着追踪消费者足迹能力的增强,营销科学正经历着一场大数据的革命.为了了解大数据背景下消费者行为和营销战略的改变,此项研究结合近十余年大数据营销的有关文献,梳理了大数据营销的相关概念,类型及分析方法,并提炼出搜索、移动、口碑、数字化、APP和社会媒体等50个大数据营销的热门主题.在此基础之上,分别从互联网、社会网络、移动互联网、大数据和人工智能等四个阶段对大数据营销的研究进展进行回顾,最后围绕客户旅程,营销活动的量化评估和营销分析技术的开发三个方面对大数据营销的未来研究方向进行展望.     

基于粗糙集和证据理论的设备状态评估方法

为准确判定复杂设备健康状态，提出一种基于粗糙集理论和证据理论的健康状态评估方法。鉴于粗糙集只能处理离散指标，首先提出一种基于动态模糊C-均值聚类算法的连续型评估指标的离散化方法；再通过基于互信息的属性约简算法对复杂设备健康状态评估指标进行约简；然后对约简的评估决策表进行处理，构建基本信度分配函数；最后利用D-S合成规则进行多指标合成得到健康状态，进一步挖掘评估指标与健康状态间的关系。实例研究及对比分析表明该方法能有效提高决策可信度，减少评估的不确定性。     

条件故障下3-元n-立方体的容错分析

研究了条件边故障下3-元n-立方体中较大连通分支点的数目，进而证明了3-元n-立方体是(4n－6)-条件边故障强Menger边连通的。最后通过一个反例说明该结果是最优的。