Forecasting Transformed Series

1976-01-01articleJournal of the Royal Statistical Society Series B (Statistical Methodology)297 citationsclosed

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Forecasting Techniques and Applications Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

Summary Suppose that a forecasting model is available for the process Xt but that interest centres on the instantaneous transformation Yt = T(Xt). On the assumption that Xt is Gaussian and stationary, or can be reduced to stationarity by differencing, this paper examines the autocovariance structure of and methods for forecasting the transformed series. The development employs the Hermite polynomial expansion, thus allowing results to be derived for a very general class of instantaneous transformations.

Citations in this library

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References

1971Time Series Analysis Forecasting and Control.

1969The Combination of Forecasts

1974Experience with Forecasting Univariate Time Series and the Combination of Forecasts

1969Prediction with a Generalized Cost of Error Function

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