GPS卫星钟差二次多项式预报模型的总体最小二乘算法

GPS精密单点定位技术是今后GPS测量技术的发展趋势,其中,卫星轨道预报与钟差预报是该技术的关键。如今,IGS组织已能够提供短时高精度的卫星轨道预报,但在钟差预报方面尚无法达到实时定位的要求,所以建立足够精度的卫星钟差预报模型尤为重要。文章根据常用的二次多项式短期预报模型,提出新的参数估计方法——总体最小二乘估计,并得出该方法能进一步提高二次多项式模型预报精度的结论。     

基于时间序列模型在物流需求预测中的应用

本文采用时间序列模型进行物流需求预测，用实例从数据平稳性的判断，平稳化、标准化，建模，经模型的识别、定价、参数估计和检验，到预测及误差和置信区间的计算，详细地说明了时间序列模型在物流需求预测中是如何应用的。结果表明该ARMA模型能够较好地拟合并可获得较高的中短期预测精度。     

Takeover prediction using forecast combinations

The ability to identify likely takeover targets at an early stage should provide investors with valuable information, enabling them to profit by investing in potential target firms. In this paper we contribute to the takeover forecasting literature by suggesting the combination of probability forecasts as an alternative method of improving the forecast accuracy in takeover prediction and realizing improved economic returns from portfolios made up of predicted targets. Forecasts from several non-linear forecasting models, such as logistic and neural network models and a combination of them, are used to determine the methodology that best reduces the out-of-sample misclassification error. We draw two general conclusions from our results. First, the forecast combination method outperforms the single models, and should therefore be used to improve the accuracy of takeover target predictions. Second, we demonstrate that an investment in a portfolio of the combined predicted targets results in significant abnormal returns being made by an investor, in the order of up to double the market benchmark return when using a portfolio of manageable size.     

Do Analysts Strategically Employ Cash Flow Forecast Revisions to Offset Negative Earnings Forecast Revisions?

We investigate whether analysts use cash flow forecasts to reduce the impact of earnings forecast revisions (EFRs) on market participants. In particular, we focus on conflict between an analyst's concurrent cash flow and earnings forecast revisions. We hypothesize and find that analysts are more likely to issue a positive cash flow forecast revision when they issue a negative earnings forecast revision concurrently, but not the opposite, particularly for Fortune 500 firms. Furthermore, our supplementary analyses suggest that (1) some analysts optimistically bias cash flow forecasts when they issue negative earnings forecast revisions; (2) the market pays less attention to the historical accuracy of analyst cash flow forecasts, so analysts have some latitude to present their cash flow forecasts in an optimistic way; and (3) the market reacts mainly to the direction, not the magnitude, of cash flow forecast revisions. Overall, these findings suggest that analysts may strategically use cash flow forecasts in conjunction with earnings forecasts to maintain good management relationships.     

Election forecasting: Too far out?

We consider two criteria for evaluating election forecasts: accuracy (precision) and lead (distance from the event), specifically the trade-off between the two in poll-based forecasts. We evaluate how much “lead” still allows prediction of the election outcome. How much further back can we go, supposing we tolerate a little more error? Our analysis offers estimates of the “optimal” lead time for election forecasts, based on a dataset of over 26,000 vote intention polls from 338 elections in 44 countries between 1942 and 2014. We find that optimization of a forecast is possible, and typically occurs two to three months before the election, but can be influenced by the arrangement of political institutions. To demonstrate how our optimization guidelines perform in practice, we consider recent elections in the UK, the US, and France.     

Bayesian forecast combination using time-varying features

In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time-varying features. Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecast combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to identify the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point forecasts and density forecasts.     

房地产价格波动的影响要素分析与趋势预测

本文围绕当前房地产市场的热点问题,应用协整理论研究了我国房地产价格与其多个影响因素之间的长期均衡关系,建立了多因素的长期均衡模型,并给出了各影响因素对房地产价格波动贡献大小的数量化测度,同时建立了相应的误差修正模型,得出全国房地产市场走势的预期。     

小议市场调查理论存在的问题及解决建议

市场调查作为现代企业管理的重要组成,越来越对企业的产品开发,价格定位等发挥重要作用,企业家们也越来越倚重于周详的市场调查与可靠的市场预测,对企业的经营进行战略决策。市场调查与预测的目的,就是为了保障经营决策的正确性和及时性。本文分析了我国市场调查业的发展现状、特点及存在问题,并探讨了我国调查业健康发展的对策。     

连云港港口货物吞吐量自适应过滤预测模型及其Excel宏实现

利用自适应过滤预测法对连云港港口2008年前几个月货物吞吐量数据建立港口吞吐量预测模型,通过VBA编程实现Excel的宏按钮操作,按照预测误差方差最小原则交互实现迭代功能,选择对最小预测误差方差贡献最小的一期对应的权值作为最佳权值进行预测,获得了精确度较高的预测结果。     

基于组合模型的用电量预测方法研究

电力是经济发展的保障,对用电量进行科学合理的预测是安排电力生产、电站建设的前提。文章基于灰色和时间序列预测模型,建立组合模型,经验证,组合模型预测精度较单一预测模型有一定程度的提高,最后利用组合模型对湖南省2010-2015年用电量进行预测。     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

The Persistence and Forecast Accuracy of Earnings Components in the USA and Japan

Not all components of earnings are expected to provide similar information regarding future earnings. For example, basic financial statement analysis indicates that the persistence of ordinary income should be greater than the persistence of special, extraordinary, or discontinued operations. Because the market assigns higher multiples to earnings components that are more persistent, differentiating earnings components on the basis of relative persistence would appear to be useful. A focus on relative predictive value is consistent with research findings and user recommendations on separating earnings components that are persistent or permanent from those that are transitory or temporary. This paper examines the persistence and forecast accuracy of earnings components for retail and manufacturing companies listed in the world's two largest equity markets; the USA and Japan. We find the forecast accuracy of earnings in both the USA and Japan increases with greater disaggregation of earnings components. The results further indicate that the improvements in forecast accuracy due to earnings disaggregation are greater in the USA than in Japan. The greater emphasis and more detailed guidelines for reporting earnings components in the USA produce a better differentiation in the persistence of earnings components resulting in greater forecast improvements from earnings disaggregation.     

上市公司制造业财务预警实证研究

选取了2003-2006年上海、深圳交易所A股市场制造业90家财务危机公司和90家所配对的正常公司为样本。选取了能够体现公司四方面业绩的8个指标,通过因子分析和判别分析,建立企业财务预警模型来对公司情况进行预测。预测精度达到83%,所建立的模型具有较好的预测效果。     

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.     

News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar

The paper proposes a novel approach to predict intraday directional-movements of currency-pairs in the foreign exchange market based on news story events in the economy calendar. Prior work on using textual data for forecasting foreign exchange market developments does not consider economy calendar events. We consider a rich set of text analytics methods to extract information from news story events and propose a novel sentiment dictionary for the foreign exchange market. The paper shows how news events and corresponding news stories provide valuable information to increase forecast accuracy and inform trading decisions. More specifically, using textual data together with technical indicators as inputs to different machine learning models reveals that the accuracy of market predictions shortly after the release of news is substantially higher than in other periods, which suggests the feasibility of news-based trading. Furthermore, empirical results identify a combination of a gradient boosting algorithm, our new sentiment dictionary, and text-features based-on term frequency weighting to offer the most accurate forecasts. These findings are valuable for traders, risk managers and other consumers of foreign exchange market forecasts and offer guidance how to design accurate prediction systems.     

电力系统中长期负荷预测方法的研究与程序设计

文章探讨了电力系统负荷的组成、特点,在分析比较常用的预测方法优缺点的基础之上,采用了灰色预测法与回归法相结合的方法建立了中长期负荷预测模型,把负荷预测工作分为2个部分:即用灰色预测法进行相关因素的预测和用回归法进行负荷预测。该模型充分利用了灰色预测法要求负荷数据少、不考虑分布规律、不考虑变化趋势、运算方便、易于检验等优点及回归法能够考虑到负荷所受的多种因素的特点,模型参数估计技术比较成熟,预测过程简单。     

Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

考虑市场力的短期边际电价预测

电价波动较负荷波动剧烈,使得整个电价的预测精度降低。造成这种价格波动的主要原因是由于在电力市场中,发电商拥有的市场力具有能够支配电价上下波动的能力,使得电价的变化更加难以预测。因此市场力在电价预测中是必须考虑的重要因素之一。提出将市场供需比指标作为电价预测的一个输入量,将其引入到预测模型中作为影响电价的因素,使预测精度得到提高。     

IE在家电制造业供应链系统研究案例

随着全球范围内市场竞争的加剧与市场需求的多元化,家电企业竞争的焦点将是如何以更低的成本和更快的市场响应速度赢得市场。C公司作为传统家电制造企业,市场覆盖遍及全球,供应链范围涉及面广;本文利用IE流程改善的思想,结合C公司的供应链现状,对供应链系统中的销售需求预测、采购管理、生产计划、仓储物流管理进行全面系统的改善,实现供应链系统的闭环无缝衔接,提升供应链效率,对市场需求作出快速响应,降低成本。