Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology

In general, due to inherently high complexity, carbon prices simultaneously contain linear and nonlinear patterns. Although the traditional autoregressive integrated moving average (ARIMA) model has been one of the most popular linear models in time series forecasting, the ARIMA model cannot capture nonlinear patterns. The least squares support vector machine (LSSVM), a novel neural network technique, has been successfully applied in solving nonlinear regression estimation problems. Therefore, we propose a novel hybrid methodology that exploits the unique strength of the ARIMA and LSSVM models in forecasting carbon prices. Additionally, particle swarm optimization (PSO) is used to find the optimal parameters of LSSVM in order to improve the prediction accuracy. For verification and testing, two main future carbon prices under the EU ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results obtained demonstrate the appeal of the proposed hybrid methodology for carbon price forecasting.     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

On the role of Prognostics and Health Management in advanced maintenance systems

The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognised as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyse how maintenance will change using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the condition-based maintenance; and (iii) how PHM can be integrated into reliability-centred maintenance programmes. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.     

Forecasting industrial production using structural time series models

Industrial production data series are volatile and often also cyclical. Hence, univariate time series models which allow for these features are expected to generate relatively accurate forecasts of industrial production. A particular class of unobservable components models — structural time series models — is used to generate forecasts of Austrian and German industrial production. A widely applied ARIMA model is used as a baseline for comparison. The empirical results show that the basic structural model generates more accurate forecasts than the ARIMA model when accuracy is measured in terms of size of error or directional change; and that the basic structural model forecasts better than the structural model with a cyclical component included on the basis of numerical measures, and tracking error for month-to-month changes.     

基于二次分解策略和模糊时间序列模型的航空客运需求预测研究

提高航空客运需求预测的准确性对于航空公司以及整个航空运输系统的发展都具有重要的现实意义。以往研究普遍采用单一分解策略去处理航空客运需求时序中存在的复杂特征,以此提升组合模型的预测性能。然而传统的分解策略存在着特征提取不完全、分解方法带有固有缺陷等问题,导致组合模型预测效果不能得到充分的提升。为此,本文提出一种基于二次分解策略和模糊时间序列模型的航空客运需求预测方法。该方法首先利用季节调整模型（X12-ARIMA）将原始时序分解成季节成分序列与季节调整后序列,继而利用改进的自适应噪声集成经验模态分解方法（ICEEMDAN）将季节调整后序列分解成一系列不同时间尺度的本征模态函数（IMF）和残差序列（Residue）。然后使用基于模糊C均值算法（FCM）划分论域区间的FTS模型对季节成分序列、各IMF分量以及残差序列分别进行预测。最后将各分量序列的预测结果进行集成,重构出航空客运需求的预测值。实证结果表明,本文所提出的二次分解策略表现显著优于传统的分解策略,并且本文所提出模型对于航空客运需求预测有着较高的准确性。     

考虑促销因素的医药电商平台需求预测研究

医药电商平台需求预测涉及到药品自身属性及电商平台推出的各种促销活动,本文针对以上影响药品销量的因素提出了时间序列-机器学习组合模型对医药电商平台进行需求预测。传统研究促销因素的需求预测文献将促销阶段商品销量拆分为常规销量和促销增量的线性组合,本文首先拟合各药品促销阶段的常规销量,根据各药品常规销量时间序列数据及服用周期,使用SARIMA模型拟合药品的常规销量预测值,并将常规销量预测值与商品促销特征数据一同输入XGBoost模型进行集成学习预测。本文使用国内某医药电商平台真实销售数据测试组合模型的有效性,结果显示组合预测模型的预测效果相比其他三种传统预测模型更优。此外,本文验证了不同折扣力度下组合预测模型的有效性,以及促销变量在预测模型中的有效性,同时研究了数据共享策略在需求预测中的应用场景,结果显示预测模型在引入促销变量和采用数据共享策略后都能显著降低模型的预测误差。     

Leveraging the Strengths of Choice Models and Neural Networks: A Multiproduct Comparative Analysis*

Choice models and neural networks are two approaches used in modeling selection decisions. Defining model performance as the out‐of‐sample prediction power of a model, we test two hypotheses: (i) choice models and neural network models are equal in performance, and (ii) hybrid models consisting of a combination of choice and neural network models perform better than each stand‐alone model. We perform statistical tests for two classes of linear and nonlinear hybrid models and compute the empirical integrated rank (EIR) indices to compare the overall performances of the models. We test the above hypotheses by using data for various brand and store choices for three consumer products. Extensive jackknifing and out‐of‐sample tests for four different model specifications are applied for increasing the external validity of the results. Our results show that using neural networks has a higher probability of resulting in a better performance. Our findings also indicate that hybrid models outperform stand‐alone models, in that using hybrid models guarantee overall results equal or better than the two stand‐alone models. The improvement is particularly significant in cases where neither of the two stand‐alone models is very accurate in prediction, indicating that the proposed hybrid models may capture aspects of predictive accuracy that neither stand‐alone model is capable of on their own. Our results are particularly important in brand management and customer relationship management, indicating that multiple technologies and mixture of technologies may yield more accurate and reliable outcomes than individual ones.     

基于多尺度分析的小麦价格预测研究

本文基于分解-重构-集成的思想,构建了一个多尺度组合预测模型,选取小麦作为粮食的代表,预测其价格走势。首先,运用集合经验模态分解方法(EEMD)分解价格序列,然后,用灰色关联分析方法对分量序列进行重构,重构为高频、中频、低频和趋势项四个部分,并从不规则因素、季节因素、重大事件和世界经济水平等方面对这四个部分波动特点进行解释,针对不同特点的分量选择不同的方法进行预测,最后对各预测结果用支持向量机集成,并与其他预测模型的预测结果进行比较。实证结果表明,本文构建的多尺度组合模型的预测效果优于灰色预测GM(1,1)、BP神经网络、SVM方法、ARIMA模型等单模型方法和ARIMA-SVM组合模型以及基于EMD和EEMD分解的其他多尺度组合模型。     

贵金属期货价格预测方法及实证研究

本文融合了二次分解与极限学习机的优势,提出了VMD-Res.-EEMD-ELM贵金属期货价格预测模型,选择变分模态分解(VMD)作为主要的分解技术,生成模态分量序列(VMFi)和残差序列(Res.),采用集合经验模态分解(EEMD)对残差序列进行二次分解,并使用具有良好泛化能力的极限学习机(ELM)对各分量进行预测,最后叠加各模态分量和残差的预测值形成收益率的最终预测结果。所提出的模型不仅充分发挥了二次分解技术的优势,而且解决了传统变分模态分解组合预测模型未考虑残差影响因素的问题。实证研究表明,本文所提出的组合模型能够全面捕捉黄金、白银期货价格日收益率序列的特征,方向性预测准确率分别为83.33%和93.33%,误差指标MAE分别为0.15和0.11,经比较本文所提出的模型具有良好的预测性能。     

A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.

     

Regression Models and Risk Estimation for Mixed Discrete and Continuous Outcomes in Developmental Toxicology

Multivariate dose-response models have recently been proposed for developmental toxicity data to simultaneously model malformation incidence (a binary outcome), and reductions in fetal weight (a continuous outcome). In this and other applications, the binary outcome often represents a dichotomization of another outcome or a composite of outcomes, which facilitates analysis. For example, in Segment II developmental toxicology studies, multiple malformation types (i.e., external, visceral, skeletal) are evaluated on each fetus; malformation status may also be ordinally measured (e.g., normal, signs of variation, full malformation). A model is proposed is for fetal weight and multiple malformation variables measured on an ordinal scale, where the correlations between the outcomes and between the offspring within a litter are taken into account. Fully specifying the joint distribution of outcomes within a litter is avoided by specifying only the distribution of the multivariate outcome for each fetus and using generalized estimating equation methodology to account for correlations due to litter clustering. The correlations between the outcomes are required to characterize joint risk to the fetus, and are therefore a focus of inference. Dose-response models and their application to quantitative risk assessment are illustrated using data from a recent developmental toxicology experiment of ethylene oxide in mice.     

A modified DEA cross efficiency method with negative data and its application in supplier selection

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.

     

Applications of extension grey prediction model for power system forecasting

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proves that the growth rate of the simulated value of the grey model GM(1,1) is a fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from GM(1,1) is the same as the primary sequence, otherwise greater error would occur. In order to overcome shortcoming of the fixed growth rates, extend the traditional GM(1,1) model by introducing linear time-varying terms, which can predict more accurately on non geometric sequences, termed EGM(1,1). Finally, compared with the other improved grey model and ARIMA model, experimental results indicate that the proposed model obviously can improve the prediction accuracy.     

基于MIDAS分位数回归的条件偏度组合投资决策

条件偏度是金融市场典型特征之一，忽略条件偏度的组合投资决策往往难以有效地分散金融风险。为此，本文构建了包含条件偏度的组合投资模型，并给出其建模方法。首先，运用MIDAS-QR模型，改善条件偏度测度效果；其次，基于CRRA效用函数，将组合投资权重设计为条件偏度和特征变量的线性组合，建立组合投资模型并给出求解方案；最后，从沪深300指数中选取10支代表性成分股进行实证研究，从收益、风险和Sharpe比率等方面，将包含条件偏度的组合投资模型与等权方案、均值-方差模型等进行比较，分析条件偏度在组合投资中的作用。实证结果表明：MIDAS-QR是测度条件偏度的有效方法，其测度结果受异常值影响小，表现稳定；条件偏度对组合投资决策具有显著影响，包含条件偏度的组合投资模型能够有效地降低投资风险、带来更高的风险调整收益。     

Linking hybrid systems to ACD models: a case study in the fibre-glass industry

This paper is aimed at formalising and testing a model for hybrid systems where the interactions between the continuous process parts and the manufacturing sub-systems are given by minor stoppages. The proposed approach consists in representing the effects of the continuous process dynamics on discrete manufacturing sub-systems by means of autoregressive conditional duration (ACD) models originally conceived to treat high-frequency and irregularly spaced financial transaction data. The model has been applied to a real-life furnace and spooling-bushing department system of a fibre-glass production plant. Results conclude that the ACD-based model proved useful for representing the occurrence of fibre-glass breakage on the spooling-bushing machines and, in more general terms, that the proposed methodology could be really suitable for the logical modelling of the hybrid production systems where the relationships between the continuous and the discrete parts are given by the occurrence of minor stoppages.     

What Risks Do People Perceive in Everyday Life? A Perspective Gained from the Experience Sampling Method (ESM)

The experience sampling method (ESM) was used to collect data from 74 part-time students who described and assessed the risks involved in their current activities when interrupted at random moments by text messages. The major categories of perceived risk were short term in nature and involved "loss of time or materials" related to work and "physical damage" (e.g., from transportation). Using techniques of multilevel analysis, we demonstrate effects of gender, emotional state, and types of risk on assessments of risk. Specifically, females do not differ from males in assessing the potential severity of risks but they see these as more likely to occur. Also, participants assessed risks to be lower when in more positive self-reported emotional states. We further demonstrate the potential of ESM by showing that risk assessments associated with current actions exceed those made retrospectively. We conclude by noting advantages and disadvantages of ESM for collecting data about risk perceptions.     

A comparison of Japanese failure models: Hybrid neural networks,logit models,and discriminant analysis

This article looks at the ability of a relatively new technique, hybrid artificial neural networks (ANNs), to predict Japanese banking and firm failures. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting failure for one year prior to the event. This suggests that for researchers, policymakers, and others interested in early warning systems, the hybrid network may be a useful tool for predicting banking and firm failures.     

混合HOGA-SVM财务风险预警模型实证研究

目前涉及遗传算法与支持向量机相结合的预测模型中,遗传算法基本上采用的是标准算法.但是在对全局函数的优化中,一般的遗传算法容易陷入局部最优,从而降低遗传算法收敛速度和搜索精度,进而影响财务风险预警模型的精度与速度.基于此,提出了基于混合全局优化正交遗传算法(HOGA)和支持向量机(SVM)的财务风险预警模型(HOGA-SVM),通过使用混合全局优化正交遗传算法连同支持向量机来改进支持向量机进行财务风险预警的效果.结果显示,提出的模型不仅提高了财务风险预警的准确率和速度,而且模型的两类分类错误率(尤其是第一类分类错误率)相对其他模型也有了明显下降.未来的工作可以把模型的应用扩大到多分类的财务风险预警问题中.     

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.