基于网络层析成像技术的道路交通流预测算法

为了更有效地预测城市路网交通流量,本文提出了一种城市道路交通预测模型.该模型基于网络层析成像(Network Tomography, NT)技术建立生成树,采用期望最大 (Expectation Maximization, EM) 算法得到路网子网车流概率分布,再结合路网子网中流量守恒原则,对待预测路段流量进行推测.实验结果表明,该模型优于现常用的人工智能模型,对城市交通流量预测更为有效,且提高了预测精度.     

旅游预测模型及应用

本文分析了旅游引力模型、多元(逐步)回归模型和时间序列模型在预测旅游人数方面的优缺点.并给出了河北省今后几年国外游客人数及美国游客人数的预测结果.对河北省旅游业的发展提出若干建议.     

Autoregressive-asymmetric moving average models for business cycle data

Much business cycle research is based on an assumption of symmetric cycles, though it is frequently argued that the downturns are steeper and more short-lived than the upturns; implying cyclical asymmetries. A new class of nonlinear autoregressive-asymmetric moving average models is introduced. These models are able to deal with symmetric as well as asymmetric phenomena. A likelihood estimation procedure and a Wald test statistic for symmetry are presented. Evidence of asymmetry is found in US real GNP growth rates.     

基于混沌吸引子的时间序列预测

本文提出一种新的时间序列预测技术。对于一个经诊断存在混沌吸引子的时间序列,根据相空间中混沌吸引子的分形等特性,建立依赖于预测点邻界状态的预测模型;综合存在于原时间序列中确定线性趋势的外推结果,实现对原时间序列的短期预测。     

基于SVR的区域经济短期预测模型

分析了区域经济发展的特性，并指出在区域经济发展预测中存在非线性强、波动性大的特点，从而导致常规的宏观经济预测手段难以取得理想的预测效果。核方法实现了数据空间与特征空间之间的非线性映射，建立在此基础上的SVR也就具备了优秀的非线性建模能力。首先对影响区域经济发展的各因素进行了相关分析，在此基础上提出并建立了基于SVR的区域经济短期预测模型，广东省江门市的应用实例说明了该模型的有效性。     

The long run,causality, and forecasting in the advertising-sales relationship

Co-integration analysis is used in a study of the advertising and sales relationship using the Lydia Pinkham data set. The series are shown to have a valid long-run relationship while Granger-causality runs in both directions. The latter is found by using a causality test involving the co-integration restrictions which seem to constitute a crucial part of such tests in the case of co-integrated variables. A comparison with previous models shows that forecasting co-integrated series is more accurate with error-correction systems, especially in the case of multi-step forecasting.     

The impact on the performance of growth curve models of changes in parameter re-estimation period and other factors

A simulation model of a real electricity supply undertaking was used to provide a financial performance measure for growth curve forecasting models. The impact on financial performance was determined when changes were made in (1) the method of estimating the model parameters, (2) the period between re-estimations, (3) the growth curve fitted and (4) the amount of smoothing of the demand time-series. The response to variation of the parameter review period was found to behave surprisingly, in that it exhibited different signs for two different estimation methods. Changes in re-estimation period explained somewhat more of the variation in performance than did a change in growth curve. Correcting the demand series for conditions which were known to be abnormal improved performance.     

Applications of time-series analysis in accounting: A review

This paper reviews research that makes use of one of the most popular forecasting methods applied in accounting: time-series analysis using the Box-Jenkins methodology. It organizes the research in the area, surveys recent applications of time-series analysis in accounting, and discusses the potential for the methodology in addressing future research issues. The emphasis is on those aspects of the accounting system that possibly cause difficulties in applying time-series methods in accounting.     

Forecasting future canadian residential heating demand: An illustration of forecasting with aggregated and disaggregated data

This paper examines the sensitivity of forecasts to the level of aggregation of the data. A relative shares regression model and a multinominal logit model are tested with both aggregate and disaggregate survey data from 2109 respondents. The results indicate the appropriate model to use depends on whether the data are disaggregate or aggregate in form. Forecasts of solar heating of dwelling unit demand and market shares are also reported for Canada in terms of the solar price relative to the natural gas price and solar reliability relative to natural gas reliability.     

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.