Modelling a traffic network with missing data

Whitlock and Queen (1998) developed a dynamic graphical model for forecasting traffic flows at a number of sites at a busy traffic junction in Kent, UK. Some of the data collection sites at this junction have been faulty over the data collection period and so there are missing series in the multivariate problem. Here we adapt the model developed in Whitlock and Queen ( 1998 ) to accommodate these missing data. Markov chain Monte Carlo methods are used to provide forecasts of the missing series, which in turn are used to produce forecasts for some of the other series. The methods are used on part of the network and shown to be very promising. Copyright © 2000 John Wiley & Sons, Ltd.     

Forecasting euro area variables with German pre‐EMU data

It is investigated whether euro area variables can be forecast better based on synthetic time series for the pre‐euro period or by using just data from Germany for the pre‐euro period. Our forecast comparison is based on quarterly data for the period 1970Q1–2003Q4 for 10 macroeconomic variables. The years 2000–2003 are used as forecasting period. A range of different univariate forecasting methods is applied. Some of them are based on linear autoregressive models and we also use some nonlinear or time‐varying coefficient models. It turns out that most variables which have a similar level for Germany and the euro area such as prices can be better predicted based on German data, while aggregated European data are preferable for forecasting variables which need considerable adjustments in their levels when joining German and European Monetary Union (EMU) data. These results suggest that for variables which have a similar level for Germany and the euro area it may be reasonable to consider the German pre‐EMU data for studying economic problems in the euro area. Copyright © 2008 John Wiley & Sons, Ltd.     

Dynamic relationship analysis of us gasoline and crude oil prices

This paper studies the dynamic relationships between US gasoline prices, crude oil prices, and the stock of gasoline. Using monthly data between January 1973 and December 1987, we find that the US gasoline price is mainly influenced by the price of crude oil. The stock of gasoline has little or no influence on the price of gasoline during the period before the second energy crisis, and seems to have some influence during the period after. We also find that the dynamic relationship between the prices of gasoline and crude oil changes over time, shifting from a longer lag response to a shorter lag response. Box-Jenkins ARIMA and transfer function models are employed in this study. These models are estimated using estimation procedure with and without outlier adjustment. For model estimation with outlier adjustment, an iterative procedure for the joint estimation of model parameters and outlier effects is employed. The forecasting performance of these models is carefully examined. For the purpose of illustration, we also analyze these time series using classical white-noise regression models. The results show the importance of using appropriate time-series methods in modeling and forecasting when the data are serially correlated. This paper also demonstrates the problems of time-series modeling when outliers are present.     

基于共形几何代数的3D医学图像配准

随着医学图像处理技术的发展,3D／3D配准益受重视,尤其是在外科手术导航等医学应用中．学者们提出了各种3D／3D配准方法,但大多方法是采用传统代数方法进行配准,配准精度和效率都存在问题．本文利用非经典数学——共形几何代数重建了3D医学图像的位置关系约柬问题,分析了医学图像的共形几何变换,构造了新的3D医学图像配准相似测度,基于此提出了新的3D医学图像配准算法,用于CT和MR图像的3D配准．新算法中,以骨骼轮廓作为配准的基础点集,在骨骼轮廓的基础上采用共形几何代数构造共形几何体,接着采用新的3D医学图像配准相似测度进行三维数据的直接配准．实验表明新算法实现了三维数据的直接对齐,能较好地定位组织器官的三维位置．可以直观地体现配准结果．     

State-space models for predicting IBNR reserve in row-wise ordered runoff triangles: Calendar year IBNR reserves & tail effects

The issue of modeling and forecasting IBNR (incurred but not reported) actuarial reserve under Kalman filter techniques and extensions, using data arranged in a runoff triangle, is a frequent theme in the literature. One quite recent approach is to order the runoff triangle under a row-wise fashion and use linear state-space models for the resulting data set. To allow new possibilities for short-term IBNR reserves as well as to mitigate insolvency risk, in this paper we extend such a state-space method by: (i) a calendar year IBNR reserve prediction; and (ii) a tail effect for the row-wise ordered triangle. The extension is implemented with a real runoff triangle and compared with some traditional IBNR predictors. Empirical results indicate that the approach of this paper outperforms the competing methods in terms of out-of-sample comparisons and gives more conservative IBNR reserves than the original state-space method.     

On estimating contemporaneous quarterly regional GDP

Subnational regional jurisdictions rarely have at their disposal a reasonable array of timely statistics to monitor their economic condition. In light of this, we develop a procedure that simultaneously estimates a quarterly time series for all regions of a country based upon quarterly national and annual regional data. While other such techniques exist, we suggest a temporal error structure that eliminates possible spurious jumps. Using our approach, regional analysts should now be able to distribute national growth among regions as soon as quarterly national figures are released. In a Spanish application, we detail some practicalities of the process and show that our proposal produces better estimates than the uniregional methods often used. Copyright © 2007 John Wiley & Sons. Ltd.     

Forecasting system energy demand

This paper presents the results of the Electric Power Research Institute Short Range Forecasting Project (EPRI-SRF) performed by the Load Forecasts Department, Economics and Forecasts Division of Ontario Hydro, Ontario, Canada. In this study a variety of short-range forecasting techniques are applied to Ontario Hydro monthly data on total system energy demand. These techniques are available in a software package (FORECAST MASTER) developed for EPRI by two consultants—Scientific Systems, Inc. (SSI) and Quantitativ Economic Research, Inc. (QUERI). The methods used for this study were the univariate Box-Jenkins method, the multivariate state-space method, Bayesian vector autoregression and autoregress ve econometric regression. A comparison of the models developed show that the econometric models perform the best overall. The state-space models are more suitable for very short-term (one-step ahead) forecasts. Although the Box-Jenkins method has the advantage of simplicity in terms of estimation and data requirement, its performance was not as good as that of the others. Bayesian vector autoregresson results indicate that this program needs some modification for monthly data.     

Observability of States in Non-Linear Systems

In this paper we define two new properties—linear observability and L-observability—to aid the study of overspecification and unidentifiability in quasi-linear and non-linear state space time series. Various equivalence results are proved and illustrated and some general properties of observable and unobservable processes are derived. The results are often proved by using graphical methods (influence diagrams) for manipulating conditional independence statements embedded in the new definitions. © 1997 John Wiley & Sons, Ltd.     

Neural network versus econometric models in forecasting inflation

Artificial neural network modelling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back‐Propagation Artificial Neural Network (BPN) models with the traditional econometric approaches to forecasting the inflation rate. Of the traditional econometric models we use a structural reduced‐form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each econometric model with a hybrid BPN model which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN models are able to forecast as well as all the traditional econometric methods, and to outperform them in some cases. Copyright © 2000 John Wiley & Sons, Ltd.     

The evaluation of measurement uncertainties and its epistemological ramifications

The way metrologists conceive of measurement has undergone a major shift in the last two decades. This shift can in great part be traced to a change in the statistical methods used to deal with the expression of measurement results, and, more particularly, with the calculation of measurement uncertainties. Indeed, as we show, the incapacity of the frequentist approach to the calculus of uncertainty to deal with systematic errors has prompted the replacement of the customary frequentist methods by fully Bayesian procedures. The epistemological ramifications of the Bayesian approach merge with a deep empiricist mood tantamount to an “epistemic turn”: measurement results are analysed in terms of degrees of belief, and central concepts such as error and accuracy are called into question. We challenge the perspective entailed by this epistemic turn: we insist on the centrality of the concepts of error and accuracy by underlining the intentional character of measurement that is intimately linked to the process of correction of experimental data. We further circumvent the difficulties posed by the classical analysis of measurement by stressing the social rather than the epistemic dimension of measurement activities.     

Evidence for the selection of forecasting methods

Reid (1972) was among the first to argue that the relative accuracy of forecasting methods changes according to the properties of the time series. Comparative analyses of forecasting performance such as the M‐Competition tend to support this argument. The issue addressed here is the usefulness of statistics summarizing the data available in a time series in predicting the relative accuracy of different forecasting methods. Nine forecasting methods are described and the literature suggesting summary statistics for choice of forecasting method is summarized. Based on this literature and further argument a set of these statistics is proposed for the analysis. These statistics are used as explanatory variables in predicting the relative performance of the nine methods using a set of simulated time series with known properties. These results are evaluated on observed data sets, the M‐Competition data and Fildes Telecommunications data. The general conclusion is that the summary statistics can be used to select a good forecasting method (or set of methods) but not necessarily the best. Copyright © 2000 John Wiley & Sons, Ltd.     

Nowcasting and predicting data revisions using panel survey data

The qualitative responses that firms give to business survey questions regarding changes in their own output provide a real‐time signal of official output changes. The most commonly used method to produce an aggregate quantitative indicator from business survey responses—the net balance or diffusion index—has changed little in 40 years. This paper investigates whether an improved real‐time signal of official output data changes can be derived from a recently advanced method on the aggregation of survey data from panel responses. We find, in a New Zealand application, that exploiting the panel dimension to qualitative survey data gives a better in‐sample signal about official data than traditional methods. Out‐of‐sample, it is less clear that it matters how survey data are quantified, with simpler and more parsimonious methods hard to improve. It is clear, nevertheless, that survey data, exploited in some form, help to explain revisions to official data. Copyright © 2009 John Wiley & Sons, Ltd.     

机械传动系统中嵌入式信号采集及无线传输设备设计与实现

针对传统的机械传动系统信号采集设备的不足,基于ARM7微处理器与射频芯片CC1101提出了用于机械传动系统的嵌入式信号采集及无线传输设备设计方案并实现。阐述了设备的组成及设计原理,并从硬件和软件两个方面说明了系统的设计及实现方法。该设备应用在某汽车传动轴扭矩测试的试验结果表明,在特殊工作环境下,设备使用方便,数据传输稳定可靠,所得信号的干扰及能量损失更小。     

基于三次B样条逆向细分的自由曲线的多分辨率表示

目前国内外关于逆向细分的研究主要集中于曲面逆向细分,对大量的特定曲线细分法的逆向细分算法研究较少,对于基于逆向细分的曲线的多分辨率构造及简化也鲜有研究.针对三次B样条细分法具有几何意义明显、规则简单等特征.本文从几何角度出发,推导并给出了基于三次B样条细分的逆向细分规则,在此基础上提出了自由曲线的一种新的多分辨率表示方法,通过在对自由曲线进行逆向细分时保留细节信息,最终可以实现自由曲线的多分辨率表示,并可应用于自由曲线的简化与精确重构中.文中给出了曲线的多分辨率表示、简化和重构的例子.该方法几何意义明显,易于编程实现.实验表明应用该逆向细分法得到的简化曲线能够更明显地反映原曲线的变化趋势.本文方法在构造分解矩阵和重构矩阵方面较以往的某些方法简单,并且在分解和重构曲线时的计算量相较于以往的方法较少.     

Forecasting for business planning: A case study of IBM product sales

This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time-series methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.     

Quantitative realizations of philosophy of science: William Whewell and statistical methods

In this paper, I examine William Whewell’s (1794–1866) ‘Discoverer’s Induction’, and argue that it supplies a strikingly accurate characterization of the logic behind many statistical methods, exploratory data analysis (EDA) in particular. Such methods are additionally well-suited as a point of evaluation of Whewell’s philosophy since the central techniques of EDA were not invented until after Whewell’s death, and so couldn’t have influenced his views. The fact that the quantitative details of some very general methods designed to suggest hypotheses would so closely resemble Whewell’s views of how theories are formed is, I suggest, a strongly positive comment on his views.     

Forecast robustness in macroeconometric models

This paper investigates potential invariance of mean forecast errors to structural breaks in the data generating process. From the general forecasting literature, such robustness is expected to be a rare occurrence. With the aid of a stylized macro model we are able to identify some economically relevant cases of robustness and to interpret them economically. We give an interpretation in terms of co‐breaking. The analytical results resound well with the forecasting record of a medium‐scale econometric model of the Norwegian economy.     

Smooth transition exponential smoothing

Adaptive exponential smoothing methods allow a smoothing parameter to change over time, in order to adapt to changes in the characteristics of the time series. However, these methods have tended to produce unstable forecasts and have performed poorly in empirical studies. This paper presents a new adaptive method, which enables a smoothing parameter to be modelled as a logistic function of a user‐specified variable. The approach is analogous to that used to model the time‐varying parameter in smooth transition models. Using simulated data, we show that the new approach has the potential to outperform existing adaptive methods and constant parameter methods when the estimation and evaluation samples both contain a level shift or both contain an outlier. An empirical study, using the monthly time series from the M3‐Competition, gave encouraging results for the new approach. Copyright © 2004 John Wiley & Sons, Ltd.     

Seasonal adjustment and kalman filtering: Extension to periodic variances

This paper reviews the relations between the methods of seasonal adjustment used by official statistical agencies and the ‘model-based’ methods that postulate explicit stochastic models for the unobserved components of a time series and apply optimal signal extraction theory to obtain a seasonally adjusted series. The Kalman filter implementation of the model-based methods is described and some recent results on its properties are reviewed. The model-based methods employ homogeneous or time-invariant models that assume in particular that the autocovariance structure does not vary with the season. Relaxing this leads to the class of models known as periodic models, and an example of a seasonally heterosceclastic unobserved-components ARIMA (SHUCARIMA) model is presented. The calculation of the standard error of a seasonally adjusted series via the Kalman filter is extended to this periodic model and illustrated for a monthly rainfall series.     

体域网中基于特征组合的步态行为识别

物联网(internet of things,IOT)拥有无处不在的识别、传感和通信能力,体域网(body area network,BAN)属于物联网中和人体相关的领域,其应用广泛,可以在日常生活中对人们进行监测及提供帮助.行走是许多日常活动的基本环节,因而步态分析能为体域网应用提供重要的生理行为信息.现有的步态分析已取得一定的研究成果,但仍存在一些问题,例如大多数步态特征提取是对加速度信号进行6重以上的变换,使得特征达到了45维以上,最后需要通过降维或优化来简化特征,较为复杂.本文设计一种灵活便捷的数据采集系统,并利用小波变换、傅里叶变换和四分位差提取出加速度信号中比较简单、低维度但能反应运动特征的步态参数,之后通过模式识别算法进行步态行为识别验证.实验结果表明该系统使用方便,特征提取方法简单实用,识别精确度为97%,EER(equal error rate)最小可到0.9%.