The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Bayesian Forecasting
Abstract
Summary: This paper describes a Bayesian approach to forecasting. The principles of Bayesian forecasting are discussed and the formal inclusion of “the forecaster” in the forecasting system is emphasized as a major feature. The basic model, the dynamic linear model, is defined together with the Kalman filter recurrence relations and a number of model formulations are given. Multi-process models introduce uncertainty as to the underlying model itself, and this approach is described in a more general fashion than in our 1971 paper. Applications to four series are described in a sister paper. Although the results are far from exhaustive, the authors are convinced of the great benefits which the Bayesian approach offers to forecasters.
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Citations
Cites
Time Series Analysis Forecasting and Control.
Bartholomew, D. J., Box, G. E. P., & Jenkins, G. M. (1971) · Operational Research Quarterly (1970-1977) · doi:10.2307/3008255 · Cited by 6,716
Techniques for Testing the Constancy of Regression Relationships Over Time
Brown, R. L., Durbin, J., & Evans, J. M. (1975) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1975.tb01532.x · Cited by 5,128
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
An Introduction to Bayesian Inference in Econometrics.
Aigner, D., & Zellner, A. (1972) · Journal of the American Statistical Association · doi:10.2307/2284674 · Cited by 1,653
The Advanced Theory of Statistics.
Kendall, M. G., & Stuart, A. (1968) · The Statistician · doi:10.2307/2986781 · Cited by 1,581
System identification—A survey
Åström, K. J., & Eykhoff, P. (1971) · Automatica · doi:10.1016/0005-1098(71)90059-8 · Cited by 1,495
Experience with Forecasting Univariate Time Series and the Combination of Forecasts
Newbold, P., & Granger, C. W. J. (1974) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344546 · Cited by 848
SOME THEOREMS IN LEAST SQUARES
PLACKETT, R. L. (1950) · Biometrika · doi:10.1093/biomet/37.1-2.149 · Cited by 433
EFFICIENT ESTIMATION OF PARAMETERS IN MOVING-AVERAGE MODELS
DURBIN, J. (1959) · Biometrika · doi:10.1093/biomet/46.3-4.306 · Cited by 401
A Bayesian Approach to Short-term Forecasting
Harrison, P. J., & Stevens, C. F. (1971) · Journal of the Operational Research Society · doi:10.1057/jors.1971.78 · Cited by 210
Self-citation
Cited by
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,629
Journal of the Royal Statistical Society (B)
Fildes, R. (1993) · International Journal of Forecasting · doi:10.1016/0169-2070(93)90088-5 · Cited by 3,437
Dynamic linear models with Markov-switching
Kim, C.-J. (1994) · Journal of Econometrics · doi:10.1016/0304-4076(94)90036-1 · Cited by 1,434
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.tm09771 · Cited by 1,049
Outliers, level shifts, and variance changes in time series
Tsay, R. S. (1988) · Journal of Forecasting · doi:10.1002/for.3980070102 · Cited by 722
Metadata
- Authors
- Harrison, P. J., & Stevens, C. F.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1976-07-01
- Volume
- 38
- Issue
- 3
- Pages
- 205-228
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W4256404094
- Cited by
- 649
- References
- 13
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
