数据色彩度预测应用研究

本文试图通过数据色彩度的模糊预测，来对其出口量进行预测研究。通过对数据的平滑拟合、演算，笔者认为该方法对于短期预测是有效和适用的。     

统计分析方法在投资项目后评价中的运用

投资项目后评价的研究是基于系统工程、反馈控制理论,运用统计预测的方法,对项目决策、实施、运营做出科学分析和判定。本文以某产能区块一次、二次加密及聚合物驱项目后评价为例,对投资项目后评价中数据资料、预测资料、等级资料的统计方法的选择进行了分析,给出了投资项目后评价中各类统计资料的适用的统计方法。     

“十一五”（2006—2010）规划期间GDP增长趋势预测

2006—2010年5年期间的经济增长速度预测属于中长期预测。中长期预测的一般方法有侧重于定量分析的经济周期分析方法与国外类比分析方法，以及侧重于定性分析的经济态势分析方法。此外还有规范分析方法与专家评估方法。本文主要利用前几种方法进行预测。     

上海港与长江航运联动发展预测研究

应用多因紊动态分析法和灰色系统预测理论，对长江流域六省二市的航运联动发展进行了预测研究。通过建立灰色系统GM（1，1）模型和对进出口贸易值进行预测，可有效克服原始数据的离散性，再利用多因素动态分析法对外贸集装箱生成量进行预测，预测效果将会大大提高。文章选择外贸集装箱生成量和进出口贸易值这两个与江海联运发展水平密切相关的指标，对上海港与长江航运联动发展进行中短期的预测，并对预测的结论进行了分析。     

家用汽车需求预测

本文首先用定性预测方法分析了家用汽车需求的主要影响因素，论证了其平稳增长的特性。然后用定量预测方法，在灰色系统模型等单一预测模型的基础上，引入了组合预测模型，通过使组合预测误差平方和最小，得到了各个单一预测方法的权重系数，建立了最优组合预测模型。对预测结果进行对比分析，验证了最优组合预测方法的准确性。最后运用所建立的最优组合预测模型对家用汽车在最近几年的需求量进行了预测。     

颠覆性技术识别与预测方法研究进展

为了把握颠覆性技术识别与预测方法研究进展，首先对颠覆性的内涵、特征和运行机制进行论述；然后，通过对已有文献进行系统梳理，将识别与预测方法分为5类，分别展开详述；最后，对各类方法进行比较分析。研究发现，已有颠覆性技术识别与预测方法仍处于发展的初期阶段，所有识别研究方法均围绕颠覆性技术的外部影响或内在特征中的某一方面展开，并未将二者有机结合起来。未来，应综合把握颠覆性技术内外部特征，构建全面、系统的识别与预测框架。     

DSS决策支持系统概述

本文介绍了DSS决策支持系统相关理论及决策的意义。决策者依据决策来指导工作，预测本部门事业未来，高效地控制企事业行为，帮助部门实现规划目标。同时也分析了运筹学与决策的关系，模型与决策的关系，以及构造决策系统的方法。     

连续梁拱桥施工监控实施方案实例

本文主要阐述了连续梁与钢管砼组合体系施工监控的方法，分析了施工监控的过程内容，对施工监控的原理和方法进行了探讨，结合现代通用预测控制理论知识，建立现场施工监控系统，通过开展结构应力和挠度监测，分析施工监控的影响因素，提出相应的预控措施，合理指导施工。     

一款简易实用的人数自动统计系统的设计

本文介绍了适用于图书馆或商场等公共场合的人数智能检测、显示系统的设计,分析了本系统的电路组成和工作原理.     

土地承包权流转方式及选择

本文对我国现有的转包、互换、出租、入股和抵押5种主要的土地承包权流转方式进行了比较分析，研究其影响因素和适用条件。总的来说，出租适用于供给方议价能力较差的情况，转包适用于收入水平较高的地区，互换则较适用于交易双方货币支付能力有限、原始承包地块分散的情况，入股适用于土地经营效率较高且遇到瓶颈的地区。抵押是土地承包权需求方融资的有效途径，抵押为杠杆经营提供了条件。     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

组合预测模型在区域物流需求预测中的应用

针对单一预测方法用于区域物流需求量预测存在的不足,文章提出了基于预测有效度的组合预测模型,即通过组合多个单一模型的预测结果,发挥各自的优点,提高预测的精确度。以广东省江门市为例,分别采用线性回归模型、灰色GM(1,1)模型和组合预测模型对其物流需求量进行了预测,实证结果表明区域物流需求组合预测模型能够取得更高的预测精度。     

D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy

This work proposes a new forecasting model to analyse the economic development of Sichuan province of China. The model, which introduces the concept of diversity, is based on an improvement of the -GMDH algorithm. The new method, called D-GMDH, is compared with two ensemble approaches which are introduced by Dutta (2009), and D-GMDH is better than the two approaches in forecasting accuracy. D-GMDH is also applied to forecast the industrial added value of the Sichuan province. The obtained results are compared with those of the traditional GMDH model, GMDH combination model and the widely used ARMA model. The results show that D-GMDH has good prediction accuracy and is an effective means for economic forecasting when data is contaminated by noise.     

河北省粮食产量预测新法探析

粮食安全对中国来说是一个重大的战略问题,是历届政府的工作重点。目前在粮食产量预测上,理论界和实践中均采用预测精度不高的单一方法,这在一定程度上影响了粮食工作的正常开展。为了提高预算精度,应采用回归和马尔可夫联合模型的方法。实践证明:采用此联合模型的方法,其预测值尤为符合实际产量。     

Fuzzy Seasonal Time Series for Forecasting the Production Value of the Mechanical Industry in Taiwan

Based on the seasonal time series ARIMA(p,d,q)(P,D,Q)_s model (SARIMA) and fuzzy regression model, we combine the advantages of two methods to propose a procedure of fuzzy seasonal time series and apply this method to forecasting the production value of the mechanical industry in Taiwan. The intention of the article is to provide the enterprises, in this era of diversified management, with a fresh method to conduct short-term prediction for the future in the hope that these enterprises can perform more accurate planning. This method includes interval models with interval parameters and provides the possibility distribution of future value. From the results of practical application to the mechanical industry, it can be shown that this method makes good forecasts. Further, this method makes it possible for decision makers to forecast the possible situations based on fewer observations than the SARIMA model and has the basis of pre-procedure for fuzzy time series.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

“十二五”时期中国水利投资预测研究

基于1952—2009年的统计数据,运用自回归移动平均模型和多项式分布滞后模型对中国"十二五"时期的国内生产总值和水利投资额进行预测。实证结果表明:多项式分布滞后模型的预测效果比仅考虑当期经济发展水平对水利投资的影响所建立的传统线性预测模型更为理想;在此基础上,运用自回归移动平均模型可预测出中国"十二五"时期的水利投资额。     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

基于"标杆管理"的海南远景经济指标预测

在海南远期经济指标的预测过程中,时间跨度较大,经济社会发展的不确定因素很多,利用传统的数学模型不易把握其发展态势,难以预测。借鉴管理学上“标杆管理”的模式成功地对海南远景经济指标进行了分析预测,该思路同样适用于其他区域经济远景指标的预测。     

Forecasting volatility in the Chinese stock market under model uncertainty

Volatility forecasting is an important issue in empirical finance. In this paper, the main purpose is to apply the model averaging techniques to reduce volatility model uncertainty and improve volatility forecasting. Six GARCH-type models are considered as candidate models for model averaging. As to the Chinese stock market, the largest emerging market in the world, the empirical study shows that forecast combination using model averaging can be a better approach than the individual forecasts.