Constrained forecasting in autoregressive time series models: A Bayesian analysis

Alba, Enrique

doi:10.1016/0169-2070(93)90057-t

Cited by 17 publications

(16 citation statements)

References 10 publications

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“…The problem of incorporating external (prior) information in the univariate ARIMA forecasts have been considered by Cholette (1982), Guerrero (1991) andde Alba (1993).…”

Section: Univariatementioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

1,265

632

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During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

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“…The problem of incorporating external (prior) information in the univariate ARIMA forecasts have been considered by Cholette (1982), Guerrero (1991) andde Alba (1993).…”

Section: Univariatementioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

1,265

632

View full text Add to dashboard Cite

show abstract

“…This result was satisfactory and corresponded to similar results establishing AR model predictive ability. Among these, the work of De Alba (1993) stands out as an extensive revision from time-series methods, where time-series prediction using a fourth-order AR model was simulated and a general efficiency of 75% observed. Hay and Pettitt (2001) obtained 58% in the analysis of twelve pneumonia-incidence time series by using a generalized first-order AR model for counting data.…”

Section: Resultsmentioning

confidence: 99%

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach

Nascimento¹,

Silva²,

Sáfadi³

et al. 2016

Genet. Mol. Res.

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ABSTRACT. We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data.

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“…In Reference [12] it is shown that when forecasting disaggregated data (say quarterly data) and given aggregate conditions (say in terms of annual data) it is possible to apply a Bayesian approach to derive conditional forecasts in the multiple regression model. The types of conditions usually considered are that the sum, or the average, of the forecasts equals a given value.…”

Section: Regression Modelmentioning

confidence: 99%

“…Related results can be found in Reference [22], who present conditional forecasts in somewhat general terms for the dynamic linear model from a Bayesian perspective. Specific Bayesian results are given in Reference [13].…”

Section: Ar(p) Modelmentioning

confidence: 99%

The Bayesian method of moments (BMOM) in some aggregation problems in econometrics

Alba

2006

Appl Stoch Models Bus & Ind

Self Cite

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The BMOM is particularly useful for obtaining post-data moments and densities for parameters and future observations when the form of the likelihood function is unknown and thus a traditional Bayesian approach cannot be used. Also, even when the form of the likelihood is assumed known, in time series problems it is sometimes difficult to formulate an appropriate prior density. Here, we show how the BMOM approach can be used in two, nontraditional problems. The first one is conditional forecasting in regression and time series autoregressive models. Specifically, it is shown that when forecasting disaggregated data (say quarterly data) and given aggregate constraints (say in terms of annual data) it is possible to apply a Bayesian approach to derive conditional forecasts in the multiple regression model. The types of constraints (conditioning) usually considered are that the sum, or the average, of the forecasts equals a given value. This kind of condition can be applied to forecasting quarterly values whose sum must be equal to a given annual value. Analogous results are obtained for AR(p) models. The second problem we analyse is the issue of aggregation and disaggregation of data in relation to predictive precision and modelling. Predictive densities are derived for future aggregate values by means of the BMOM based on a model for disaggregated data. They are then compared with those derived based on aggregated data.

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Constrained forecasting in autoregressive time series models: A Bayesian analysis

Cited by 17 publications

References 10 publications

25 years of time series forecasting

25 years of time series forecasting

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach

The Bayesian method of moments (BMOM) in some aggregation problems in econometrics

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