A new methodology for carbon price forecasting in EU ETS

This paper proposes a new methodology for carbon price forecasting. It posits a finite distributed lag (FDL) model and then applies a GA‐ridge algorithm to determine a set of proper predictors with coefficient estimates. An empirical study was conducted in the European Union Greenhouse Gas Emissions Trading market, revealing that our methodology not only yields good forecasting results but also provides some interesting analysis on the carbon price market. It turns out that the combination of the FDL model and GA‐ridge algorithm is desirable for forecasting and analyzing the complicated carbon price market because of its capability of selecting proper predictors from a class of predictors of itself.     

改进EEMD-GRU混合模型在径流预报中的应用

为解决径流预测模型存在的预测精确度低、稳定性差、延时高等问题,结合门控制循环单元神经网络（gated recurrent unit, GRU）,集合经验模态分解（ensemble empirical mode decomposition, EEMD）的各自优点,提出一种基于改进EEMD方法的深度学习模型（EEMD-GRU）。该模型首先以智能算法对径流信号进行边界拓延,以解决EEMD边界效应。然后利用改进EEMD方法将径流信号分解为若干稳态分量,将各分量作为GRU模型的输入并对其进行预测。实验结果表明,与结合了经验模态分解的支持向量回归模型相比,并行EEMD-GRU径流预测模型的预测精准度、可信度和效率分别提高82.50%、144.67%和95.49%。基于EEMD-GRU的最优运算结果表明,该方法可进一步减少区域防洪的经济损失,提高灾害监管的工作效率。     

二层分解技术在电价预测中的应用研究

针对电价波动幅度大且预测精度低的问题,提出了二层分解技术与神经网络组成的电价多步预测模型。该模型首先采用集合经验模式分解将原始电价序列分解为一系列分量,变分模态分解将第一层分解产生的最高频率分量进一步分解为一系列模态分量,所有分量采用神经网络模型进行预测,并使用纵横交叉算法对神经网络的参数进行优化,最后叠加所有子序列,得出预测电价值。仿真结果表明,所提出的模型相比其他混合模型具有更好的预测性能,且实用价值高。     

Correlation and hierarchies in financial markets

As an emerging financial market, the trading value of carbon emission trading market has definitely increased in recent years. The carbon emission is not only trading in carbon emitters but also has become an important investment target. To determine the mechanism of this growing market, we analyzed the EU allowances (EUA) price series in European Climate Exchange (ECX) that is the leading European emissions futures market. As other financial market, the absolute value of price change (volatility) in carbon emission trading market also shows long-term power-law correlations. Our analysis shows that definite cross correlations exist between EUA and many other markets. These cross correlations exist in wild-range fields, stock market index, futures of crude, sugar, cocoa, etc., suggesting that in this new carbon emission trading market the speculation behavior had already become a main factor that can affect the price change.     

A study on the market instability index and risk warning levels in early warning system for economic crisis

Early warning system (EWS) can be treated as a pattern recognition problem since the distinctive feature of economic crisis makes it possible to distinguish critical and normal economic situations using a pattern classifier. Although the most works in EWS are mainly focused on training and pattern classifier, little attention has been paid to the effective indices or feature variables that allow closer look and analysis about the current instability nature of the economic crisis. This paper proposes to utilize market instability index (MII) and stepwise risk warning levels that can diagnose the current instability of the stock market to foretell how the current stock market will proceed in advance. This approach allows the proper policy actions to be taken for the possible financial crisis according to different risk warning levels of instability. Through empirical examples with Korean stock market and Greece stock market, the proposed method demonstrates its potential usefulness in an early warning system.     

An improved EEMD method based on the adjustable cubic trigonometric cardinal spline interpolation

The empirical mode decomposition (EMD) has recently emerged as an efficient tool to adaptively decompose non-stationary signals for nonlinear systems, which has a wide range of applications such as automatic control, mechanical engineering and medicine and biology. A noise-assisted variant of EMD named ensemble empirical mode decomposition (EEMD) have been proposed to alleviate the mode mixing phenomenon. In this paper, we proposed an improved EEMD method, namely cardinal spline interpolation based EEMD (C-EEMD), by optimizing the sifting procedure. Specifically, we employ the adjustable cubic trigonometric cardinal spline interpolation (CTCSI) to accurately represent free curves, other than the original one used in the traditional EEMD. The new interpolation approach can be used to build the mean curve in a more precise way. By virtue of CTCSI, we can therefore obtain the mean value curve from midpoints of the local maxima and minima by just one interpolation operations, which saves almost half the computational cost. Extensive experimental results on synthetic data and real EMI signals clearly demonstrate the superiority of the proposed method, compared to the state-of-the-arts.     

A hybrid VMD–BiGRU model for rubber futures time series forecasting

As one of the four major industrial raw materials in the world, natural rubber is closely related to the national economy and people’s livelihood. The analysis of natural rubber price and volatility can give hedging guidance to manufacturers and provide investors with uncertainty and risk information to reduce investment losses. To effectively analyses and forecast the natural rubber’s price and volatility, this paper constructed a hybrid model that integrated the bidirectional gated recurrent unit and variational mode decomposition for short-term prediction of the natural rubber futures on the Shanghai Futures Exchange. In data preprocessing period, time series is decomposed by variational mode decomposition to capture the tendency and mutability information. The bidirectional gated recurrent unit is introduced to return the one-day-ahead prediction of the closing price and 7-day volatility for the natural rubber futures. The experimental results demonstrated that: (a) variational mode decomposition is an effective method for time series analysis, which can capture the information closely related to the market fluctuations; (b) the bidirectional neural network structure can significantly improve the model performance both in terms of fitting performance and the trend prediction; (c) a correspondence was found between the predicted target, i.e., the price and volatility, and the intrinsic modes, which manifested as the impact of the long-term and short-term characteristics on the targets at different time-scales. With a change in the time scale of forecasting targets, it was found that there was some variation in matching degree between the forecasting target and the mode sub-sequences.     

基于非结构数据流行学习的碳价格多尺度组合预测

碳交易价格的有效预测对制定符合国情的碳金融市场政策以及碳金融市场的风险管理都具有重要意义.对此,提出一种基于非结构数据流行学习的碳价格多尺度组合预测方法.首先,利用网络搜索指数提取碳价格相关的非结构化数据,基于等度量映射流行学习对其进行降维;然后,对降维后的非结构化数据、其他影响因素结构化数据、碳交易价格分别进行经验模态分解(Empirical mode decomposition,EMD),得到不同个数的本征模函数(Intrinsic mode function,IMF),并采用Fine-to-coarse方法对IMF进行重构,得到高频序列、低频序列和趋势项;最后,利用自回归积分滑动平均模型(Autoregressive integrated moving average model,ARIMA)、偏最小二乘(Partial least squares,PLS)回归和神经网络对高频数据、低频数据和趋势项进行预测,将3种预测结果进行集成,得到最终预测值.仿真实验结果表明,所提出的方法可以有效利用多源信息,具有较高的预测精度和良好的适用性.     

港口动力煤价格模型

从替代、需求、供给、库存、惯性5个方面创建了影响我国港口动力煤价的有关因素的计量模型。利用2005年1月至2012年6月的月度数据揭示了相关因素的量化影响作用,发现上月煤价、煤炭生产成本以及港口库存等是最为显著的影响因素。在此基础上,考虑到近几年煤炭市场的剧烈波动和明显的季节性特征,一方面展开了以2008年发生的国际金融危机为临界点的对比分析工作,另一方面提出了以铁路货运周转量的周期性变化为参考的淡旺季划分方法,揭示了各影响因素在淡旺季对港口动力煤价的差异作用,以及这种差异性在危机前后的变化。最后,综合宏观经济环境、煤炭市场力量演化以及行为经济学等原因对模型结果进行了深刻剖析,提出了有益的管理启示。     

基于改进HHT的风力发电系统轴承故障诊断

轴承是风力发电机组中故障率较高的部件,其故障信号为非线性、非平稳信号,经验模态分解是一种自适应的信号处理方法,可用来分析此类信号,但是模态混叠使得经验模态分解无法准确地将固有模态分离出来。针对此问题,采用总体平均经验模态分解进行改进,利用高斯白噪声的频率均匀分布的统计特性,抑制模态混叠现象,并通过计算固有模态函数与故障信号的互信息来剔除虚假分量,从而得到更准确的Hilbert-Huang谱,由此提取故障信息,判断故障类型。仿真试验及轴承故障诊断实例均证明了该方法的有效性。     

EEMD方法在刀具磨损状态识别的应用

总体经验模态分解(EEMD)方法在EMD的基础上消除了模态混叠的现象,从而更能准确地揭露出信号特征信息。根据声发射信号的非稳态、非线性的特点,提出一种基于EEMD应用于刀具磨损状态识别的方法。通过EEMD获取无模态混叠的IMF分量;通过敏感度评估算法从所有IMF分量中提取敏感的IMF;提取敏感IMF的能量作为支持向量机(SVM)分类器的输入,将刀具分成正常切削、中期磨损和严重磨损3种状态。通过比较EEMD与应用EMD等方法的分类准确率,确立了基于EEMD的方法在提取刀具磨损状态特征信息的优势。     

基于CEEMD阈值和相关系数原理的MEMS陀螺信号去噪方法

针对经验模态分解（EMD）方法易产生模态混叠问题,而集成经验模态分解（EEMD）方法又存在重构误差较大的缺陷,提出了一种基于完备集成经验模态分解（CEEMD）阈值滤波和相关系数原理的MEMS陀螺信号去噪方法。首先通过CEEMD方法对陀螺信号进行有效完备的分解,并利用相关系数原理合理确定分解后噪声分量与有效分量的界限。在此基础上,通过借鉴小波阈值处理方式和EMD阈值设置方法,对信号进行阈值滤波去噪。对仿真信号和实际MEMS陀螺信号的研究结果表明,CEEMD阈值去噪方法的去噪效果要优于CEEMD、EEMD、EMD强制去噪方法和小波分析方法。这也充分体现了其在MEMS陀螺信号去噪应用中的可行性和有效性。     

基于EEMD的车辆微动信号提取及分类

针对轮式和履带式车辆微动信号的差异对目标车辆进行了识别分类,利用集合经验模式分解(EEMD)将原始信号分解为多个本征模函数(IMF),通过相关性分析,验证了EEMD能够有效克服EMD所带来的模态混叠问题.在此基础上,提取了4种特征,采用最近邻方法进行分类.实验结果表明:经EEMD所提取的特征是有效的,对目标速度,以及方位角的变化具有相当的稳健性.     

基于白噪声统计特性与EEMD的高速列车横向减振器故障诊断*

针对高速列车横向减振器故障信号非线性非平稳的特点,提出了基于白噪声统计特性与聚合经验模态分解（EEMD）相结合的故障诊断算法。首先,利用经验模态分解（EMD）对故障信号进行去噪,然后对去噪后的信号进行EEMD分解,最后对用相关系数求得的最能反映振动信号的本征模态函数（IMF）计算排列组合熵。在240km/h速度下,对高速列车横向减振器7种工况进行诊断,识别率达到91.8%。实验结果表明：与基于小波熵特征分析的算法相比,该算法具有更高的识别率和更强的抗噪性能。     

A stochastic approximation algorithm for option pricing model calibration with a switchable market

This paper is concerned with option pricing under a regime-switching model. The switching process takes two different modes, and the underlying stock price evolves in accordance with the two modes dictated by a continuous-time, finite-state Markov chain. At any given instance, the price follows either a geometric Brownian motion model or a mean-reversion model, depending on its market mode. Stochastic approximation/optimization algorithms are developed for model calibration. Convergence of the algorithm is proved; rate of convergence is also provided. Option market data are used to predict the future market mode.     

基于EEMD和关联维数的小电流接地故障选线方法

针对小电流接地系统发生单相接地故障时,各线路零序电流的非平稳、非线性等复杂特性,提出一种基于总体模态分解(EEMD)和关联维数相结合的选线方法.EEMD算法是在经验模态分解(EMD)的基础上加以改进,能够消除模态混叠现象,同时保留了经验模态分解具有的良好的时频特性;EEMD能根据信号本身的特点对瞬时出现的信号进行分析,并将信号分解成若干个固有模态函数(IMF)分量和一个剩余分量.利用关联维数不易受噪声干扰特点,对分解的IMF信号分量进行处理,采用G-P算法计算关联维数,通过比较关联维数的大小选出发生故障的线路.仿真结果表明,该选线方法可靠性高且效果较好.     

An extended Fuzzy-AHP approach to rank the influences of socialization-externalization-combination-internalization modes on the development phase

Continuous innovation intended to deliver products with new attributes is an imperative driver for organizations to remain competitive in today’s fast changing market. A successful innovation is often associated with adoption and execution of all SECI (socialization-externalization-combination-internalization) modes of knowledge creation within any Product Development (PD) phase. This article is an attempt to argue with the general notion and to distinguish different PD phases’ affinity corresponding to distinct SECI modes. In this regard, the paper proposes an extended Fuzzy Analytic Hierarchy Process (EFAHP) approach to determine the ranking in which any PD phase is influenced from SECI modes. In the EFAHP approach, the complex problem of knowledge creation is first itemized into a simple hierarchical structure for pairwise comparisons. Next, a triangular fuzzy number (TFN) concept is applied to capture the inherent vagueness in linguistic terms of a decision-maker. This paper recommends mapping TFNs with normal distributions about X-axis. This allows us to develop a mathematical formulation to estimate the degree of possibility (importance value) when two TFNs do not intersect with each other (current techniques in literature calculate this value as zero). In order to demonstrate the applicability and usefulness of the proposed EFAHP in ranking SECI modes, an empirical study of development phase is considered. Five criteria and their 19 sub-criteria for measuring the phase’s performance are identified based on both an extensive discussion with subject matter experts and rigorous literature survey. After stringent analysis, we found that the mode that highly influenced the development phase is ‘combination mode’. The article discusses the application of lean tools that can be employed to improve the knowledge creation process.     

EEMD分解在电力系统故障信号检测中的应用

针对经验模态分解(EMD)的希尔伯特-黄变换(HHT)在电力系统故障信号检测问题,应用存在的模态混叠会导致扰动信号检测失效,为此提出一种基于聚类经验模型分解(EEMD)的故障信号检测的方法。方法通过多次对目标数据加入随机白噪声序列以保证不同区域信号映射的完整性,并且克服了传统EMD分解造成的模态混叠问题,通过EEMD方法提取信号的固有模态函数(IMF),再进行Hilbert变换,利用Hilbert谱对故障暂态和扰动时刻进行检测,通过瞬时频率实现对故障暂态和扰动时刻的准确定位。通过数字仿真分析表明,方法是准确有效的。     

An EEMD-based multi-scale fuzzy entropy approach for complexity analysis in clean energy markets

To measure the efficiency of clean energy markets, a multi-scale complexity analysis approach is proposed. Due to the coexisting characteristics of clean energy markets, the “divide and conquer” strategy is introduced to provide a more comprehensive complexity analysis framework for both overall dynamics and hidden features (in different time scales), and to identify the leading factors contributing to the complexity. In the proposed approach, ensemble empirical mode decomposition (EEMD), a competitive multi-scale analysis tool, is first implemented to capture meaningful features hidden in the original market system. Second, fuzzy entropy, an effective complexity measurement, is employed to analyze both the whole system and inner features. In empirical analysis, the nuclear energy and hydropower markets in China and US are investigated, and some interesting results are obtained. For overall dynamics, the US clean energy markets appear a significantly higher complexity level than China’s markets, implying market maturity and efficiency of US clean energy relative to China. For inner features, similar features (in terms of similar time scales) in different markets present similar complexity levels. For different inner features, there are some distinct differences in clean energy markets between US and China. China’s markets are mainly driven by upward long-term trends with a low-level complexity, while short-term fluctuations with high-level complexity are the leading features for the US markets. All these results demonstrate that the proposed EEMD-based multi-scale fuzzy entropy approach can provide a new analysis tool to understand the complexity of clean energy markets.     

An effective procedure for the distribution of magazines

The article provides theoretical and empirical findings on developing an effective procedure for magazine single copy distribution. Considerations of determinants of demand are provided, emphasizing the importance of price and seasonality. A relative adjustment formula for stockout corrections is introduced, applicable in connection with normally distributed demand. The main contribution is a new demand forecasting and supply decision model, taking explicitly into account price and seasonality. The applicability and potential economic benefits are shown by giving results from an extensive empirical test in the Finnish single copy market.