Forecasting energy prices: Quantile-based risk models

The goal of this paper is to use a new modelling approach to extract quantile-based oil and natural gas risk measures using quantile autoregressive distributed lag mixed-frequency data sampling (QADL-MIDAS) regression models. The analysis compares this model to a standard quantile auto-regression (QAR) model and shows that it delivers better quantile forecasts at the majority of forecasting horizons. The analysis also uses the QADL-MIDAS model to construct oil and natural gas prices risk measures proxying for uncertainty, third-moment dynamics, and the risk of extreme energy realizations. The results document that these risk measures are linked to the future evolution of energy prices, while they are linked to the future evolution of US economic growth.     

A new approach to forecasting intermittent demand based on the mixed zero-truncated Poisson model

This paper proposes a new approach to forecasting intermittent demand by considering the effects of external factors. We classify intermittent demand data into two parts—zero value and nonzero value—and fit nonzero values into a mixed zero-truncated Poisson model. All the parameters in this model are obtained by an EM algorithm, which regards external factors as independent variables of a logistic regression model and log-linear regression model. We then calculate the probability of occurrence of zero value at each period and predict demand occurrence by comparing it with critical value. When demand occurs, we use the weighted average of the mixed zero-truncated Poisson model as predicted nonzero demands, which are combined with predicted demand occurrences to form the final forecasting demand series. Two performance measures are developed to assess the forecasting methods. By presenting a case study of electric power material from the State Grid Shanghai Electric Power Company in China, we show that our approach provides greater accuracy in forecasting than the Poisson model, the hurdle shifted Poisson model, the hurdle Poisson model, and Croston's method.     

Using the yield curve to forecast economic growth

This paper finds the yield curve to have a well-performing ability to forecast the real gross domestic product growth in the USA, compared to professional forecasters and time series models. Past studies have different arguments concerning growth lags, structural breaks, and ultimately the ability of the yield curve to forecast economic growth. This paper finds such results to be dependent on the estimation and forecasting techniques employed. By allowing various interest rates to act as explanatory variables and various window sizes for the out-of-sample forecasts, significant forecasts from many window sizes can be found. These seemingly good forecasts may face issues, including persistent forecasting errors. However, by using statistical learning algorithms, such issues can be cured to some extent. The overall result suggests, by scientifically deciding the window sizes, interest rate data, and learning algorithms, many outperforming forecasts can be produced for all lags from one quarter to 3 years, although some may be worse than the others due to the irreducible noise of the data.     

A likelihood ratio and Markov chain-based method to evaluate density forecasting

In this paper, we propose a likelihood ratio-based method to evaluate density forecasts, which can jointly evaluate the unconditional forecasted distribution and dependence of the outcomes. Unlike the well-known Berkowitz test, the proposed method does not require a parametric specification of time dynamics. We compare our method with the method proposed by several other tests and show that our methodology has very high power against both dependence and incorrect forecasting distributions. Moreover, the loss of power, caused by the nonparametric nature of the specification of the dynamics, is shown to be small compared to the Berkowitz test, even when the parametric form of dynamics is correctly specified in the latter method.     

On the directional predictability of equity premium using machine learning techniques

This paper applies a plethora of machine learning techniques to forecast the direction of the US equity premium. Our techniques include benchmark binary probit models, classification and regression trees, along with penalized binary probit models. Our empirical analysis reveals that the sophisticated machine learning techniques significantly outperformed the benchmark binary probit forecasting models, both statistically and economically. Overall, the discriminant analysis classifiers are ranked first among all the models tested. Specifically, the high-dimensional discriminant analysis classifier ranks first in terms of statistical performance, while the quadratic discriminant analysis classifier ranks first in economic performance. The penalized likelihood binary probit models (least absolute shrinkage and selection operator, ridge, elastic net) also outperformed the benchmark binary probit models, providing significant alternatives to portfolio managers.     

城市居民生活水价测算的回归分析

国内外学术界普遍关注的是水价测算模型及其参数估计的研究,印研究水价预测模型及应用,但鲜于对预测后的水价进行回归分析,造成调整后的水价组成不明,无法为水费合理分配提供科学依据.依据长期边际成本原理,通过分析苏州市1997-2008年单位供水可变成本与固定成本实际数据,测算2010年苏州市居民生活水价,并对预测后的水价进行...     

Equipment Maintenance Material Demand Forecasting Based on Gray-Markov Model

Maintenance material reserves must keep an appropriate scale,in order to meet the possible demand of support objectives.According to the sequence of maintenance material consumption,this paper establishes a Gray-Markov forecasting model by combining Gray system theory and Markov model. Few data are needed in the proposed Gray-Markov forecasting model which has high prediction precision by involving small parameters. The performance of Gray-Markov forecasting model was demonstrated using practical application and the model was proved to be a valid and accurate forecasting method. This Gray-Markov forecasting model can provide reference for making material demand plan and determining maintenance material reserves.     

A deep residual compensation extreme learning machine and applications

The extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC-ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self-noise prediction. We used the DRC-ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC-ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA-RELM and OS-ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back-propagation neural network (BPNN).     

AERMOD大气预测模式在焦化项目环境影响预测中的应用

简要介绍了AERMOD模式及其运行流程,阐述了AERMOD大气预测模式在焦化项目环境影响评价中的应用。     

基于Theil不等系数的IOWGA算子组合预测模型

将Theil不等系数和IOWGA算子相结合,提出一种基于Theil不等系数的IOWGA算子最优组合预测模型,并定义了优性组合预测和非劣性组合预测的概念,最后通过对税收收入进行组合预测说明该组合预测方法的有效性和合理性,且该模型存在优性组合预测.