我国1995年物价形势分析及物价指数预测

首先对我国1994年物价大幅度上涨的成因进行了分析,然后根据物价上涨成因的多样性与复杂性,依据Box-Jenkins模型对1995年物价形势进行了预测。预测结果经检验合格并与实际比较吻合。最后对如何控制1995年物价上涨提出了几点看法。     

基于模糊Box-Jenkins方法的电价建模与短期预测

由于单一的AR、MA和ARMA模型不能很好地匹配复杂的电价时间序列数据,因此传统的Box-Jenkins方法不能很好地进行电价预测。文章提出了基于模糊Box-Jenkins的电价建模和短期预测方法。引入模糊策略,生成分别对应Box-Jenkins方法中的AR,MA,ARMA三个模型的模糊因子,再通过模糊因子对三个模型进行模糊综合。对浙江省电力市场电价数据的仿真表明,在电价序列不能较好地匹配Box-Jenkins方法中各模型的情况下,模糊Box-Jenkins方法能取得更好的预测效果。     

Understanding the Kalman Filter

This is an expository article. Here we show how the successfully used Kalman filter, popular with control engineers and other scientists, can be easily understood by statisticians if we use a Bayesian formulation and some well-known results in multivariate statistics. We also give a simple example illustrating the use of the Kalman filter for quality control work.     

A simulation study and evaluation of multivariate forecast based control charts applied to ARMA processes

Much research had been performed in the area of control charting techniques for monitoring autocorrelated processes, especially regarding forecast based monitoring schemes. Forecast based monitoring schemes involve fitting an appropriate time-series model to the process, generating one step ahead forecast errors, and monitoring the forecast errors with traditional control charts. Another method introduced into the literature involves using multivariate control charts to monitor the ARMA derived one-step-ahead (OSA) and two-step-ahead (TSA) forecast errors. This article provides a broad simulation study and evaluation of the suggested multivariate approaches in regards to various ARMA(1,1) and AR(1) processes, and a comparison to their univariate counterparts.     

Nonparametric Forecasting in Time Series—A Comparative Study

The problem of predicting a future value of a time series is considered in this article. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables, smoothing parameter selection, and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behavior of the methods is also illustrated by a detailed analysis of two data sets.     

畜产品价格定量预测方法评析

通过文献研究,本文归纳出定量预测畜产品价格的方法模型主要有回归模型、Box-Jenkins方法和VAR模型以及神经网络模型。没有明确的证据显示某一种方法或模型一定优于另一种方法或模型。在畜产品价格预测中,如果需要得到较为精确的预测结果,可供借鉴的做法是采用两种以上的方法或模型进行比较,或者进行组合。     

On the pade tablé and its relationship to the r and sarrays and arm a modeling

The Box-Jenkins method is a popular and important technique for modeling and forecasting of time series. Unfortunately the problem of determining the appropriate ARMA forecasting model (or indeed if an ARMA model holds) is a major drawback to the use of the Box-Jenkins methodology. Gray et al. (1978) and Woodward and Gray (1979) have proposed methods of estimating p and qin ARMA modeling based on the R and Sarrays that circumvent some of these modeling difficulties.

In this paper we generalize the R and S arrays by showing a relationship to Padé approximunts and then show that these arrays have a much wider application than in just determining model order. Particular non-ARMA models can be identified as well. This includes certain processes that consist of deterministic functions plus ARMA noise, indeed we believe that the combined R and S arrays are the best overall tool so fur developed for the identification of general 2nd order (not just stationary) time scries models.