运用区间预测法控制财务预测误差

财务预测为财务预算提供依据,是企业经营管理工作的基础,而定量预测在财务预测的理论实践中具有极其重要的地位,但定量预测容易产生误差。本文从分析误差产生的原因着手,引进具有很强生命力的区间数学为工具,提出财务管理的区间预测方法,具体探讨区间指数平滑法的运用。     

基于时间序列分析对矿坑涌水量的区间预测

基于时间序列分析建立了矿坑涌水量的模型,通过误差分析做出区间估计,把预测结果表述为区间形式,并提出了模型相对精度的概念。在桃源煤矿矿坑涌水量的预测中,同自回归模型AR(11)对比,该模型将预测精度分别从3.95%和4.35%提高到1.00%和1.44%。     

广义加权对数平均组合预测模型的性质与应用

针对组合预测模型能够充分利用各种单项预测方法提供的信息,从而具有模拟和预测精度较高的优点,研究和探讨了广义加权对数平均组合预测模型的最优化理论基础及其数学性质,在组合预测模型预测值与单项预测方法预测值的P次幂误差平方和最小的基础上建立了广义加权对数平均组合预测模型,并推导出最优权的计算公式。最后通过实例证明了该模型的有效性和实用性。     

土岩结合地层暗挖区间隧道下穿建筑物变形预测研究

本文结合了青岛地铁一期工程(3号线)土岩结合地层某浅埋暗挖区间隧道下穿既有建筑物这一工程,采用MIDAS-GTS二维平面应变弹塑性非线性方法进行分析,对土岩结合地层暗挖隧道上方建筑物的安全性进行预测。工程最大难点在于隧道下穿既有建筑物的施工期间不能影响建筑物的正常运营。施工中应在该范围内及时进行初期支护、封闭成环并及时进行初支背后回填注浆。分析结果对土岩结合地层区间隧道下穿既有建筑物分析研究提供借鉴,对后续工程的施工具有重要参考价值。     

检验分析中误差的来源及消除

本文阐述了误差产生的主要来源及消除方法，尽可能将误差减小到允许的范围内，以提高分析结果的准确度和精密度     

公司理财区间分析法的运用

将区间分析法运用于公司理财及相关数值的计算,不仅可以得到近似值而且可以得到误差范围。本文重点研究了公司投资决策的区间净现值法,并运用区间综合评估法对产品质量进行了评估。     

基于银行间国债回购利率时间序列的实证分析

预测市场利率的走势，对于商业银行利率风险管理非常重要。依据国债7天和14天回购利率数据，本文建立了利率预测综合自回归移动平均模型(ARIMA)和误差修正模型(ECM)。模拟结果表明，ARIMA模型不太理想，而ECM模型效果较好。     

技术交易规则预测能力与收益率动态过程——基于Bootstrap方法的实证研究

本文对我国股票市场技术交易规则预测能力进行了实证检验,发现移动平均规则所产生的买入区间收益率更大而波动率却更小,卖出区间的收益率为负而波动率却更大。运用自举(Bootstrap)方法检验发现,四种常用的收益率线性模型均不能解释买卖出区间收益率与波动率所表现出的非对称现象,尤其无法解释卖出区间收益率为负的现象。为此,本文通过人工神经网络方法,将条件异方差结构引入到现有的收益率非线性模型,发现该模型能更好地解释买卖出区间收益率与波动率模式,表明收益率动态过程中存在非线性特征。     

多轴多工位数控机床加工精度误差预测方法研究

预测加工精度误差可以提前发现误差并采取相应的措施，减少废品和次品的产生，从而提高产品质量。基于此，本文以德国的“DMG”(DMG-100P)数控机床作为案例，研究了多轴多工位数控机床加工精度误差预测方法，采用模型对机床加工过程中的误差进行了预测，并进行了实验验证。研究方法包括数据采集、神经网络模型构建与预测、实验验证等。研究结果表明，所建立的模型能够较为准确地预测多轴多工位数控机床的加工精度误差，为提高机床加工精度和效率提供了有价值的参考。     

基于组合模型的某农场电商生鲜农产品需求预测研究

为实现某农场生鲜农产品需求量的精准预测,文章基于Sharply值权重分配法构建ARIMA-SVM组合预测模型,并采用误差分析等方式证明预测方法的可行性、有效性。结果表明：Sharply值组合预测模型克服了ARIMA模型与SVM模型在局部区间内预测精度欠佳的弊端,能够应用于生鲜农产品需求量完整性、可靠性的需求量预测;组合模型的预测结果可以为农场生鲜农产品产销提供理论指导。     

Empirical prediction intervals revisited

Empirical prediction intervals are constructed based on the distribution of previous out-of-sample forecast errors. Given historical data, a sample of such forecast errors is generated by successively applying a chosen point forecasting model to a sequence of fixed windows of past observations and recording the associated deviations of the model predictions from the actual observations out-of-sample. The suitable quantiles of the distribution of these forecast errors are then used along with the point forecast made by the selected model to construct an empirical prediction interval. This paper re-examines the properties of the empirical prediction interval. Specifically, we provide conditions for its asymptotic validity, evaluate its small sample performance and discuss its limitations.     

Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals

This paper evaluates the performances of prediction intervals generated from alternative time series models, in the context of tourism forecasting. The forecasting methods considered include the autoregressive (AR) model, the AR model using the bias-corrected bootstrap, seasonal ARIMA models, innovations state space models for exponential smoothing, and Harvey’s structural time series models. We use thirteen monthly time series for the number of tourist arrivals to Hong Kong and Australia. The mean coverage rates and widths of the alternative prediction intervals are evaluated in an empirical setting. It is found that all models produce satisfactory prediction intervals, except for the autoregressive model. In particular, those based on the bias-corrected bootstrap perform best in general, providing tight intervals with accurate coverage rates, especially when the forecast horizon is long.     

The tourism forecasting competition

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

The asymmetry of judgemental confidence intervals in time series forecasting

In forecasting a time series, one may be asked to communicate the likely distribution of the future actual value, often expressed as a confidence interval. Whilst the accuracy (calibration) of these intervals has dominated most studies to date, this paper is concerned with other possible characteristics of the intervals. It reports a study in which the prevalence and determinants of the symmetry of judgemental confidence intervals in time series forecasting was examined. Most prior work has assumed that this interval is symmetrically placed around the forecast. However, this study shows that people generally estimate asymmetric confidence intervals where the forecast is not the midpoint of the estimated interval. Many of these intervals are grossly asymmetric. Results indicate that the placement of the forecast in relation to the last actual value of a time series is a major determinant of the direction and size of the asymmetry.     

VAR,ERROR CORRECTION AND PRETEST FORECASTS AT LONG HORIZONS

This paper focuses on the construction of forecasts over long horizons where a typical long-horizon forecast might span four years using 20 to 40 years’ data. It is argued that the presence of persistence in the form of unit or near-unit autoregressive roots poses substantial difficulties for long-horizon interval and point forecasting. These difficulties may not be overcome even by efficient pre-testing or model-selection procedures and might, in general, lead to point forecasts with large asymptotic root mean squared errors and undesirably wide prediction intervals.     

主成分分析—BP神经网络在备件需求预测中的应用

备件需求预测在装备维修保障中占据重要的地位,针对当前主要以经验为主进行估计,与实际需求相差较大,提出基于主成分分析—BP神经网络模型的备件需求预测方法。首先利用主成分分析方法去除原始输入数据的相关性,降低数据维度,减小网络规模,选择合适的隐含层的BP神经网络。最后通过结合实例进行分析,取得较好的效果。     

Using forecast evaluation to improve the accuracy of the Greenbook forecast

Recently, Patton and Timmermann (2012) proposed a more powerful kind of forecast efficiency regression at multiple horizons, and showed that it provides evidence against the efficiency of the Fed’s Greenbook forecasts. I use their forecast efficiency evaluation to propose a method for adjusting the Greenbook forecasts. Using this method in a real-time out-of-sample forecasting exercise, I find that it provides modest improvements in the accuracies of the forecasts for the GDP deflator and CPI, but not for other variables. The improvements are statistically significant in some cases, with magnitudes of up to 18% in root mean square prediction error.     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

Considerations of a retail forecasting practitioner

Forecasts can be used in an extraordinarily diverse range of ways across many domains in which forecasting practitioners work continuously towards improving their forecasts. Each of these domains may require the analysis of different kinds of inputs and special considerations. Even within a given domain, such as retail, there may be many similar use cases of the same kind of forecast, which can lead to practitioners making different decisions. This paper discusses several of the important decision points that practitioners must work through and uses item-level sales forecasting in the retail domain as leveraged by pricing and inventory management as examples of the different paths that may be taken. It considers how each use can lead to a different forecasting objective, and a corresponding focus on different error metrics. In addition, there are several tradeoffs in the forecasting methods that are used to meet each of the objectives best, including the kinds of models used, the running time speed, and forecast accuracy requirements.