Info-gap forecasting and the advantage of sub-optimal models

We consider forecasting in systems whose underlying laws are uncertain, while contextual information suggests that future system properties will differ from the past. We consider linear discrete-time systems, and use a non-probabilistic info-gap model to represent uncertainty in the future transition matrix. The forecaster desires the average forecast of a specific state variable to be within a specified interval around the correct value. Traditionally, forecasting uses a model with optimal fidelity to historical data. However, since structural changes are anticipated, this is a poor strategy. Our first theorem asserts the existence, and indicates the construction, of forecasting models with sub-optimal-fidelity to historical data which are more robust to model error than the historically optimal model. Our second theorem identifies conditions in which the probability of forecast success increases with increasing robustness to model error. The proposed methodology identifies reliable forecasting models for systems whose trajectories evolve with Knightian uncertainty for structural change over time. We consider various examples, including forecasting European Central Bank interest rates following 9/11.     

基于人体营养健康角度的中国果蔬发展建模

我国食物生产在一定程度上依然不能适应营养需求,居民营养不足与过剩并存。为了解决这个问题,本文将数据范围定位在常见的果蔬品种上,对其所含营养成分进行主成分分析和聚类,选择主要的蔬菜水果。利用损耗率和马尔可夫链,用线性回归的方法,通过对以往数据的分析,构建模型预测果蔬的消费量。进一步地,构建线性最优化模型来确定不同经济区域、不同季度的主要蔬菜水果的最合理消费量和购买成本。基于居民人体的营养均衡、购买成本、种植者收益、进出口贸易以及土地面积等多方面因素的考虑,构建多目标规划模型,寻找最优的产量和消费量。从种植产量、价格、国民营养摄入等方面向有关部门提出合理化建议。     

一种基于局部回归的多尺度碳市场价格预测模型研究

碳市场价格呈现非线性、非平稳的复杂特性,准确预测具有较大的挑战。基于“分而治之”的思想,提出了一种基于局部回归的多尺度碳市场价格预测模型。提出的模型利用集成经验模态分解(EEMD)对碳市场价格时间序列进行分解。启发于EEMD局部特征分解的特点,对分解后的分量采用局部回归方法进行预测,然后将分量预测结果进行集成。采用的局部回归方法包括局部线性回归(LLP)、局部多项式回归、局部岭回归、局部主成分回归、局部偏最小二乘回归和局部套索回归。实验结果表明基于局部回归的多尺度预测模型具有优异的预测性能。在提出的模型中,EEMD-LLP结构简单且性能更为突出,进一步对EEMD-LLP参数的适应性进行探讨。与新近提出模型的对比结果表明了EEMD-LLP在碳市场价格预测中的有效性。     

Estimating the term structure of mortality

In modeling and forecasting mortality the Lee-Carter approach is the benchmark methodology. In many empirical applications the Lee-Carter approach results in a model that describes the log central death rates by means of linear trends. However, due to the volatility in (past) mortality data, the estimation of these trends, and, thus, the forecasts based on them, might be rather sensitive to the sample period employed. We allow for time-varying trends, depending on a few underlying factors, to make the estimates of the future trends less sensitive to the sampling period. We formulate our model in a state-space framework, and use the Kalman filtering technique to estimate it. We illustrate our model using Dutch mortality data.     

A better project performance prediction model using fuzzy time series and data envelopment analysis

Earned value management (EVM) is a critical project management methodology that evaluates and predicts project performance from cost and schedule perspectives. The novel theoretical framework presented in this paper estimates future performance of a project based on the past performance data. The model benefits from a fuzzy time series forecasting model in the estimation process. Furthermore, fuzzy-based estimation is developed using linguistic terms to interpret different possible conditions of projects. Eventually, data envelopment analysis is applied to determine the superior model for forecasting of project performance. Multiple illustrative cases and simulated data have been used for comparative analysis and to illustrate the applicability of theoretical model to real situations. Contrary to EVM-based approach, which assumes the future performance is the same as the past, the proposed model can greatly assist project managers in more realistically assessing prospective performance of projects and thereby taking necessary and on-time appropriate actions.     

混合区间多尺度分解的区间时间序列组合预测

鉴于传统预测方法一直基于“点”来衡量时间序列数据,然而现实生活中在给定的时间段内许多变量是有区间限制的,点值预测会损失波动性信息。因此,本文提出了一种基于混合区间多尺度分解的组合预测方法。首先,建立区间离散小波分解方法(IDWT)、区间经验模态分解方法(IEMD)和区间奇异普分析方法(ISSA)。其次,用本文构建的IDWT、IEMD和ISSA对区间时间序列进行多尺度分解,从而得到区间趋势序列和残差序列。然后,用霍尔特指数平滑方法(Holt's)、支持向量回归(SVR)和BP神经网络对区间趋势序列和残差序列进行组合预测得到三种分解方法下的区间时间序列预测值。最后,用BP神经网络对各预测结果进行集成得到区间时间序列最终预测值。同时,为证明模型的有效性进行了AQI空气质量的实证预测分析,结果表明,本文所提出基于混合区间多尺度分解的组合预测方法具有较高的预测精度和良好的适用性。     

A model equation for nonlinear wavelength selection and amplitude evolution of frontal waves

Summary We study a model equation describing the temporal evolution of nonlinear finite-amplitude waves on a density front in a rotating fluid. The linear spectrum includes an unstable interval where exponential growth of the amplitude is expected. It is shown that the length scale of the waves in the nonlinear situation is determined by the linear instabilities; the effect of the nonlinearities is to limit the amplitude's growth, leaving the wavelength unchanged. When linearly stable waves are prescribed as initial data, a short interval of rapid decrease in amplitude is encountered first, followed by a transfer of energy to the unstable part of the spectrum, where the fastest growing mode starts to dominate. A localized disturbance is broken up into its Fourier components, the linearly unstable modes grow at the expense of all other modes, and final amplitudes are determined by the nonlinear term. Periodic evolution of linearly unstable waves in the nonlinear situation is also observed. Based on the numerical results, the existence of low-order chaos in the partial differential equation governing weakly nonlinear wave evolution is conjectured.     

Fuzzy interaction regression for short term load forecasting

Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field in the forecasting community. During the past 30 plus years, many statistical and artificial intelligence techniques have been applied to short term load forecasting (STLF) with varying degrees of success. Although fuzzy regression has been tried for STLF for about a decade, most research work is still focused at the theoretical level, leaving little value for practical applications. A primary reason is that inadequate attention has been paid to the improvement of the underlying linear model. This application-oriented paper proposes a fuzzy interaction regression approach to STLF. Through comparisons to three models (two fuzzy regression models and one multiple linear regression model) without interaction effects, the proposed approach shows superior performance over its counterparts. This paper also offers critical comments to a notable but questionable paper in this field. Finally, tips for practicing forecasting using fuzzy regression are discussed.     

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??In this paper, the multivariate linear statistical method is applied to research the undergraduate grades of students from the school of mathematics in Hefei University of Technology, and explore the impact on the later achievement by the early stage of achievement from all undergraduate courses. First, we get the main components from the previous courses by principal component analysis, then construct a linear regression model between the later achievement and main components by the stepwise regression method. Next, a linear regression model between the later achievement and the early stage of achievement from all undergraduate courses is constructed by Adaptive-Lasso method. Finally, comparative analysis is performed for the result of the above models. The research shows that the principal component regression model based on the Adaptive-Lasso method can well fit the later achievement, and give a reasonable explanation for the later academic performance.     

Modelling Sales

This paper is concerned with some aspects of direct and indirect sales rates of products, and with a model based on the sales matrix. The purpose of this consideration is to determine, through indirect sales rates, the structure of sales and to construct a model which may be used for planning and forecasting goals. In other words, by means of the sales matrix, or a model based on it, we can estimate the future sales movements. This can be done either from the assumption that past relations will be kept approximately in the same proportions, or that they will change in the future. In each case all the changes can be described through the system based on the sales matrix.     

恒生指数期货收盘价的预测分析

本文针对中国证券市场沪深300指数期货即将试行的情况，用多元统计分析的多元回归分析方法，探讨影响恒生指数期货走势的相关因素，并在此基础上，提出恒生指数期货（以下简称恒指期货）的回归预测模型，据此可对恒指期货的收盘价进行预测，供管理层和投资者在实际操作中参考。     

城市表层土壤重金属污染分析模型

本文先利用Matlab做出各种重金属元素浓度的空间分布图,初步得到土壤重金属污染的状况.接着用内梅罗污染指数法定量的确定土壤重金属污染最严重的地区,并用主成分分析法进行了验证.最后利用灰色-灾变与回归预测的组合模型解决了地质环境的演变问题.     

On Optimal Forecasting with Soft Computation for Nonlinear Time Series

In this paper we present a new approach on optimal forecasting by using the fuzzy set theory and soft computing methods for the dynamic data analysis. This research is based on the concepts of fuzzy membership function as well as the natural selection of evolution theory. Some discussions in the sensitivity of the design of fuzzy processing will be provided. Through the design of genetic evolution, the AIC criteria is used as the adjust function, and the fuzzy memberships function of each gene model are calculated. Simulation and empirical examples show that our proposed forecasting technique can give an optimal forecasting in time series analysis.     

基于ARIMA和LSSVM的非线性集成预测模型

针对复杂时间序列预测困难的问题,在综合考虑线性与非线性复合特征的基础上,提出一种基于ARIMA和最小二乘支持向量机(LSSVM)的非线性集成预测方法.首先采用ARIMA模型进行时间序列线性趋势建模,并为LSSVM建模确定输入阶数;接着根据确定的输入阶数进行时间序列样本重构,采用LSSVM模型进行时间序列非线性特征建模;最后采用基于LSSVM的非线性集成技术形成一个综合的预测结果.将该方法用于中国GDP预测取得的结果,与单独预测方法及流行的其他集成预测方法相比,预测精度有了较大的提高,从而验证了方法的有效性和可行性.     

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.     

基于品牌价值的销售额预测模型应用研究

首先分析了企业的品牌价值与其销售额的变化情况,归纳出品牌价值与销售额之间的两个重要的变化规律.而后以此作为二者关系方程构建的基础假设,以广告反映模型(VW模型)为基础,将原模型中的广告投入变量替换为品牌价值变量,建立了品牌价值与销售额之间的关系方程.通过求解关系方程的平衡解,得到了以品牌价值为变量的销售额预测模型.最后,采用海尔集团的实际数据对所建模型的适用性和正确性进行了证明,同时还对模型的预测精度进行了检验.研究突破了以往研究一般采用时间、环境变量直接预测销售额,或是采用历史数据对销售额进行模拟预测等传统的预测方法,首次从品牌价值的角度出发研究其对销售额的影响途径及影响力度.品牌价值是能够包涵如广告投入等因素的全部信息量的重要指标,因此其能够更为全面客观地衡量商品在整个市场中的价值潜力、能够更准确地反映其未来的销售份额.     

基于主成分回归模型的经济增长因素分析

在经济增长因素分析中,常用多元回归分析方法,但有时建立的回归模型拟合效果不好或不合理。为此本文给出建立主成分回归分析的方法。本文对经济增长给出两种回归分析方法,即建立主成分线性回归模型,分析经济增长的边际效应,建立主成分非线性回归模型,分析经济增长的弹性效应,实例表明效果很好。     

Development of improved adaptive approaches to electricity demand forecasting

This paper develops a short-term forecasting system for hourly electricity load demand based on Unobserved Components set up in a State Space framework. The system consists of two options, a univariate model and a non-linear bivariate model that relates demand to temperature. In order to handle the rapidly sampling interval of the data, a multi-rate approach is implemented with models estimated at different frequencies, some of them with ‘periodically amplitude modulated’ properties. The non-linear relation between demand and temperature is identified via a Data-Based Mechanistic approach and finally implemented by Radial Basis Functions. The models also include signal extraction of daily and weekly components. Both models are tested on the basis of a thorough experiment in which other options, like ARIMA and Artificial Neural Networks are also used. The models proposed compare very favourably with the rest of alternatives in forecasting load demand.     

基于模糊时间序列的传感器网络感知数据预测模型

传感器网络监控系统属于大型复杂系统,由感知节点以一定的时间间隔向sink节点发送感知数据,以实现对应用环境的监控。由于网络本身及应用环境的影响,得到的感知数据往往存在不确定性。此外,周期性报告数据模式影响到实时监控数据的精确性。本文应用时间序列模型预测传感器数据以响应用户查询,可有效降低网络通信量。通过对无线传感器网络的数据分析,引入多属性模糊时间序列预测模型,充分考虑了无线传感器网络时间序列中存在的趋势因素,并提出了适合于传感器网络的修正预测模型。实验结果表明模糊时间序列模型可有效预测传感器网络数据,且能提高预测精度。     

On interval portfolio selection problem

The future returns of each securities cannot be correctly reflected by the data in the past, therefore the expert’s judgements and experiences should be considered to estimate the security returns for the future. In this paper, we propose an interval portfolio selection model in which both the returns and the risks of assets are defined as intervals. By using interval and convex analysis, we solve this model and get the noninferior solution. Finally, an example is given to illustrate our results. The interval portfolio selection model improves and generalizes the Markowitz’s mean-variance model and the results of Deng et al. (Eur J Oper Res 166(1):278–292, 2005).