摘要:

AbstractA 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 top

ISSN:0277-6693    

DOI:10.1002/for.3980030302

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980030303

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980030304

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980030305

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

摘要:

AbstractThe parsimonious method of exponentially weighted regression (EWR) is attractive but limited in application because it depends upon just one discount factor. This paper generalizes the EWR approach to a method called discount weighted estimation (DWE) which allowed distinct model components to have different associated discount factors. The method includes EWR as a special case. The general non‐limiting recurrence relationships will be useful in practice, especially when practitioners wish to specify prior information, to intervene with subjective judgement and to derive estimates and forecasts sequentially based upon limited data. Two theorems extend the important EWR limiting results of Dobbie and McKenzie to DWE. The latter permits the derivation of a large class of known processs for which DWE is optimal. The method is illustrated by two applications, one of which uses the famous international airline passenger data. This allows a comparision with the ICI MULDO system which uses a particular two discount factor forecasting method. A companion paper extends the discount methods to Bayesian forecasting, Kalman filtering and state space modellin

ISSN:0277-6693    

DOI:10.1002/for.3980030306

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

摘要:

AbstractIn this paper different ways to identify the order of the Box–Jenkins transfer function model are discussed. The discussion concerns estimation of the impulse response weight function in the case of more than one input variable. It is found that most of the existing methods are either unsuitable when there is more than one input variable, or expensive or difficult to use. To overcome these deficiencies an extended regression method is proposed. The new method is based on the solution of some problems in connection with the use of the regression method. The impulse response weights are estimated by a biased regression estimator on variables transformed with respect to the noise model. To test the new approach a small simulation experiment has been performed. The results from the simulations indicate that the proposed method may be of value to the practitione

ISSN:0277-6693    

DOI:10.1002/for.3980030307

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

摘要:

AbstractWe compare univariate and multivariate forecasts based on ARMA models. In theory we cannot do worse by using a multivariate model instead of a univariate one, but we can risk getting no improvement. Conditions for no improvements are discussed as well as cases where large improvements occur. The effect of estimated parameters is examined and found to be small granted that a good method of estimation is used. However, multivariate models could be very sensitive to structural changes. This is illustrated via an example involving monetary data, where the multivariate forecasts perform considerably worse than the univariate ones. This seems to put a limitation on the use of multivariate ARMA forecasting models.

ISSN:0277-6693    

DOI:10.1002/for.3980030308

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

摘要:

AbstractHill and Woodworth (1980) proposed an algorithm suitable for identifying Box–Jenkins models automatically without reliance on the investigator. This paper first reviews the method. It is then used on the 111 series analysed by Anderson in the Makridakis forecasting competition. The results show that the automatic method of Hill and Woodworth is comparable in terms of accuracy to the full Box–Jenkins identification proced

ISSN:0277-6693    

DOI:10.1002/for.3980030309

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

摘要:

AbstractCAPRI is a fully automatic and quick procedure for forecasting. It is based on the Box–Jenkins methodology and needs noa prioriknowledge 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 proces

ISSN:0277-6693    

DOI:10.1002/for.3980030310

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY

摘要:

AbstractThe 111 series of the Makridakis competition are used to address a number of questions pertaining to use of the Box–Jenkins technique. The ARIMA models developed are compared to the ARIMA models developed independently by Andersen for the Makridakis competition. The time required to perform the analysis for each series is discussed in terms of model complexity. Forecast accuracy, measured as the MAPE for the one step ahead forecast, is discussed for different series length

ISSN:0277-6693    

DOI:10.1002/for.3980030311

出版商:John Wiley&Sons, Ltd.    

年代:1984

数据来源: WILEY