A unified view of statistical forecasting procedures
- 1 July 1984
- journal article
- research article
- Published by Wiley in Journal of Forecasting
- Vol. 3 (3) , 245-275
- https://doi.org/10.1002/for.3980030302
Abstract
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.Keywords
This publication has 49 references indexed in Scilit:
- Estimating Missing Observations in Economic Time SeriesJournal of the American Statistical Association, 1984
- The Signal Extraction Approach to Nonlinear Regression and Spline SmoothingJournal of the American Statistical Association, 1983
- The accuracy of extrapolation (time series) methods: Results of a forecasting competitionJournal of Forecasting, 1982
- An ARIMA-Model-Based Approach to Seasonal AdjustmentJournal of the American Statistical Association, 1982
- Maximum Likelihood Fitting of ARMA Models to Time Series With Missing ObservationsTechnometrics, 1980
- Two Methods for Examining the Stability of Regression CoefficientsJournal of the American Statistical Association, 1977
- Intervention Analysis with Applications to Economic and Environmental ProblemsJournal of the American Statistical Association, 1975
- The use of trend curves as an aid to market forecastingIndustrial Marketing Management, 1972
- Discrete square root filtering: A survey of current techniquesIEEE Transactions on Automatic Control, 1971
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960