The M-competition with a fully automatic box–jenkins procedure

CAPRI is a fully automatic and quick procedure for forecasting. It is based on the Box–Jenkins methodology and needs no a priori knowledge about the time series. The 1001 series of the Makridakis competition have been analysed with this program and its accuracy measured in comparison with other methods. CAPRI is recommended for short term forecasting horizons in cases where the user does not want to interfere with the modelling process.     

Application of intervention analysis to a road fatality series in ontario

Effect of an intervention on a road fatality time series is studied using (i) the usual intervention analysis of Box and Tiao (1975), and (ii) CUSUM charts for the one-step-ahead forecast errors. It is shown that the seat belt and speed limit legislations of the Ontario Government had some impact in bringing down the road toll level.     

基于时间的通信导航兼备系统定位误差分析

通信导航兼备系统已成为未来通信导航系统发展方向 ,由美国发展起来的联合战术信息分发系统(JTIDS)是典型代表。研究了通信和导航发展的历程及互相结合的途径 ,分析了JTIDS的工作方式和信号特征 ,讨论了JTIDS通信和导航能力 ;运用导航定位误差分析理论着重对JTIDS的相对导航原理及定位误差进行了深入分析 ,首次给出了该系统的定位误差场分布图和系统工作区。研究和分析结果可为建设和发展通信导航兼备系统提供重要参考     

利用GDOP对蜂窝移动通信系统移动台定位的方法

针对基于网络的到达时间 (TOA)定位系统 ,分析了定位误差与移动台 (MS)位置及参数测量误差的关系 ,结合实际中能同时接收到移动台信号的基站 (BS)数有限、定位误差对移动台位置敏感的特点 ,提出了直接用解析法计算所有位置线的交点 ,然后用几何淡化因子 (GDOP)及参数测量误差的均方差对交点进行加权平均来估计移动台位置的方法。该方法同时考虑了参数误差与几何淡化因子对定位精度的影响 ,不需要矩阵求逆 ,运算量小 ,速度快。适用于存在直达波 (LOS)信号的蜂窝通信环境。     

流水施工组织的工期计算

本文通过实例着重探讨在流水施工中各种情况的工期计算方法。     

A super-resolution algorithm for spectral estimation and time series extrapolation

Recent developments in the signal processing field of electrical engineering have resulted in several frequency domain methods of extrapolating a time series. Insight gained in testing one such method, the Papoulis algorithm, has been used to suggest modifications which greatly improve its performance under most operating conditions where real data are concerned. The modified Papoulis method thus developed has been applied to electricity load forecasting over the short and medium term, as well as to world economic and energy data, to assess the cyclic structure present in each series about a trend.     

Linear constraints,robust-weighting and efficient composite modeling

Recent studies have shown that composite forecasting produces superior forecasts when compared to individual forecasts. This paper extends the existing literature by employing linear constraints and robust regression techniques in composite model building. Security analysts forecasts may be improved when combined with time series forecasts for a diversified sample of 261 firms with a 1980-1982 post-sample estimation period. The mean square error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts in constrained and unconstrained ordinary least squares regression models. These reductions are very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by ARIMA (0,1,1) processes. Moreover, security analysts' forecast errors may be significantly reduced when constrained and unconstrained robust regression analyses are employed.     

Forecast comparisons of four models of U.S. interest rates

In this paper we compare the out of sample forecasts from four alternative interest rate models based on expanding information sets. The random walk model is the most restrictive. The univariate time series model allows for a richer dynamic pattern and more conditioning information on own rates. The multivariate time series model permits a flexible dynamic pattern with own- and cross-series information. Finally, the forecasts from the MPS econometric model depend on the full model structure and information set. In theory, more information is preferred to less. In practice, complicated misspecified models can perform much worse than simple (also probably misspecified) models. For forecasts evaluated over the volatile 1970s the multivariate time series model forecasts are considerably better than those from simpler models which use less conditioning information, as well as forecasts from the MPS model which uses substantially more conditioning information but also imposes ‘structural’ economic restrictions.     

A unified view of statistical forecasting procedures

A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.