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A New Version Of Structural Persistence In Prediction

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  • Adi Raveh

Abstract

. A data analysis estimation method called structural persistence is presented in this paper. Prediction applications of the method to time series with trend and seasonal components are discussed. The basic underlying assumption is that the structure of a given series does not change in the forecasting range. Therefore, when forecasts are made, the values which measure the structure (e.g. the trend shape) of the original and of the forecast‐extended time series should be the same. We propose relaxed ‘model‐free’ use of the principle which nevertheless provides an explicit prediction formula for the one‐stepahead prediction Pn+1. Missing data can also be estimated using this method. The procedure is applied to some previously published time series data and the prediction results are compared with those obtained using the Box‐Jenkins approach.

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  • Adi Raveh, 1989. "A New Version Of Structural Persistence In Prediction," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(1), pages 77-93, January.
    • Handle: RePEc:bla:jtsera:v:10:y:1989:i:1:p:77-93
    DOI: 10.1111/j.1467-9892.1989.tb00017.x
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    Cited by:

    1. Ingram Olkin & Adi Raveh, 2009. "Bounds for how much influence an observation can have," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 1-11, March.

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